nits: compute the conv slot order in the graph and keep the n_head name of bailingmoe3

graph: store the conv state of every rollback arch in one set_rows
The rollback slots of the causal conv history were written with one copy per slot in qwen4exp, glm5-next, kimi-k3, bailingmoe3, lfm and mamba2, while delta_net already wrote them with a single set_rows. build_conv_state_store now does this for all of them: one copy without rollback, otherwise one set_rows of the overlapping windows into the min(n, n_rs_seq + 1) slots a rollback can read. The slot order {n_rs_seq, ..., 0} is computed once per graph and each conv layer takes a view of its tail. Its size does not depend on the ubatch, so the reserved graph covers every ubatch shape, and the graph input that held it is gone. A model that is not causal never rolls back, so the context clamps n_rs_seq to 0 for it, like for an arch without rollback support.
2026-11-04 09:57:44 -06:00 · 2026-11-04 15:37:14 +01:00 · 2026-11-04 15:37:14 +01:00 · 2026-11-04 12:00:37 +02:00 · 2026-11-04 12:00:37 +02:00 · 2026-11-04 11:23:25 +02:00
407 changed files with 66063 additions and 21915 deletions
@@ -13,6 +13,8 @@ on:
      '**/*.hpp',
      '**/*.c',
      '**/*.cpp',
+      '**/*.py',
+      '**/requirements*.txt',
    ]

  pull_request:
@@ -24,7 +26,9 @@ on:
      '**/*.h',
      '**/*.hpp',
      '**/*.c',
-      '**/*.cpp'
+      '**/*.cpp',
+      '**/*.py',
+      '**/requirements*.txt',
    ]

 cache-mode: none
@@ -69,7 +73,7 @@ jobs:
        run: |
          python3 -m venv .venv
          source .venv/bin/activate
-          pip install jinja2==3.1.6
+          pip install -r requirements/requirements-convert_hf_to_gguf.txt

      - name: ccache-buckets-restore
        uses: ./.github/actions/ccache-buckets
@@ -134,7 +138,7 @@ jobs:
        run: |
          python3 -m venv .venv
          source .venv/Scripts/activate
-          pip install jinja2==3.1.6
+          pip install -r requirements/requirements-convert_hf_to_gguf.txt

      - name: Build
        id: cmake_build
@@ -156,3 +160,13 @@ jobs:
          source .venv/Scripts/activate
          cd build
          ctest -C Release -L behavior -LE 'model|backend' --verbose --timeout 900
+
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        env:
+          GH_TOKEN: ${{ github.token }}
+        with:
+          key: behavior-check-windows-2025
+          older: 5m
+          min: 1
+          dry-run: ${{ github.event_name != 'push' || github.ref != 'refs/heads/master' }}
@@ -0,0 +1,160 @@
+name: CI (apple - daily)
+
+on:
+  workflow_dispatch: # allows manual triggering
+  schedule:
+    - cron: '0 0 * * *'
+
+cache-mode: none
+permissions:
+  contents: read
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
+env:
+  GGML_NLOOP: 3
+  GGML_N_THREADS: 1
+  LLAMA_ARG_LOG_COLORS: 1
+  LLAMA_ARG_LOG_PREFIX: 1
+  LLAMA_ARG_LOG_TIMESTAMPS: 1
+
+jobs:
+  macos-latest-ios-xcode:
+    runs-on: macos-latest
+
+    permissions:
+      actions: write
+      contents: read
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v6
+
+      - name: Setup Xcode
+        uses: ggml-org/setup-xcode@v1
+        with:
+          xcode-version: latest-stable
+
+      - name: Build
+        id: cmake_build
+        run: |
+          sysctl -a
+          cmake -B build -G Xcode \
+            -DGGML_METAL_EMBED_LIBRARY=ON \
+            -DLLAMA_OPENSSL=OFF \
+            -DLLAMA_BUILD_APP=OFF \
+            -DLLAMA_BUILD_EXAMPLES=OFF \
+            -DLLAMA_BUILD_TOOLS=OFF \
+            -DLLAMA_BUILD_TESTS=OFF \
+            -DLLAMA_BUILD_SERVER=OFF \
+            -DCMAKE_SYSTEM_NAME=iOS \
+            -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
+            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
+          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
+
+      - name: xcodebuild for swift package
+        id: xcodebuild
+        run: |
+          ./build-xcframework.sh
+
+      - name: Upload xcframework artifact
+        uses: actions/upload-artifact@v6
+        with:
+          name: llama-xcframework
+          path: build-apple/llama.xcframework/
+          retention-days: 1
+
+      - name: Build Xcode project
+        run: |
+          xcodebuild -downloadPlatform iOS
+          xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' FRAMEWORK_FOLDER_PATH=./build-ios build
+
+  macos-latest-tvos:
+    runs-on: macos-latest
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Build
+        id: cmake_build
+        run: |
+          sysctl -a
+          cmake -B build -G Xcode \
+            -DGGML_METAL_EMBED_LIBRARY=ON \
+            -DLLAMA_BUILD_COMMON=OFF \
+            -DLLAMA_BUILD_APP=OFF \
+            -DLLAMA_BUILD_EXAMPLES=OFF \
+            -DLLAMA_BUILD_TOOLS=OFF \
+            -DLLAMA_BUILD_TESTS=OFF \
+            -DLLAMA_BUILD_SERVER=OFF \
+            -DCMAKE_SYSTEM_NAME=tvOS \
+            -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
+            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
+          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
+
+  macos-latest-visionos:
+    runs-on: macos-latest
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Build
+        id: cmake_build
+        run: |
+          sysctl -a
+          cmake -B build -G Xcode \
+            -DGGML_METAL_EMBED_LIBRARY=ON \
+            -DLLAMA_BUILD_COMMON=OFF \
+            -DLLAMA_BUILD_APP=OFF \
+            -DLLAMA_BUILD_EXAMPLES=OFF \
+            -DLLAMA_BUILD_TOOLS=OFF \
+            -DLLAMA_BUILD_TESTS=OFF \
+            -DLLAMA_BUILD_SERVER=OFF \
+            -DCMAKE_SYSTEM_NAME=visionOS \
+            -DCMAKE_OSX_DEPLOYMENT_TARGET=1.0 \
+            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
+          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
+
+  macos-latest-swift:
+    runs-on: macos-latest
+    needs: macos-latest-ios-xcode
+
+    permissions:
+      actions: read
+      contents: read
+
+    strategy:
+      matrix:
+        destination: ['generic/platform=macOS', 'generic/platform=iOS', 'generic/platform=tvOS']
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Download xcframework artifact
+        uses: actions/download-artifact@v7
+        with:
+          name: llama-xcframework
+          path: build-apple/llama.xcframework/
+
+      - name: Build llama.cpp with CMake
+        id: cmake_build
+        run: |
+          sysctl -a
+          cmake -B build -G Xcode \
+            -DGGML_METAL_EMBED_LIBRARY=ON \
+            -DLLAMA_OPENSSL=OFF \
+            -DLLAMA_BUILD_APP=OFF \
+            -DLLAMA_BUILD_EXAMPLES=OFF \
+            -DLLAMA_BUILD_TOOLS=OFF \
+            -DLLAMA_BUILD_TESTS=OFF \
+            -DLLAMA_BUILD_SERVER=OFF \
+            -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64"
+          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
@@ -107,140 +107,3 @@ jobs:
          # ref: https://github.com/ggml-org/llama.cpp/pull/19802#issuecomment-4013704023
          ctest -L main -E "test-llama-archs|test-llama-context|test-speculative-rejection-sampling" --verbose --timeout 900

-  macos-latest-ios-xcode:
-    runs-on: macos-latest
-
-    permissions:
-      actions: write
-      contents: read
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v6
-
-      - name: Setup Xcode
-        uses: ggml-org/setup-xcode@v1
-        with:
-          xcode-version: latest-stable
-
-      - name: Build
-        id: cmake_build
-        run: |
-          sysctl -a
-          cmake -B build -G Xcode \
-            -DGGML_METAL_EMBED_LIBRARY=ON \
-            -DLLAMA_OPENSSL=OFF \
-            -DLLAMA_BUILD_APP=OFF \
-            -DLLAMA_BUILD_EXAMPLES=OFF \
-            -DLLAMA_BUILD_TOOLS=OFF \
-            -DLLAMA_BUILD_TESTS=OFF \
-            -DLLAMA_BUILD_SERVER=OFF \
-            -DCMAKE_SYSTEM_NAME=iOS \
-            -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
-            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
-          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
-
-      - name: xcodebuild for swift package
-        id: xcodebuild
-        run: |
-          ./build-xcframework.sh
-
-      - name: Upload xcframework artifact
-        uses: actions/upload-artifact@v6
-        with:
-          name: llama-xcframework
-          path: build-apple/llama.xcframework/
-          retention-days: 1
-
-      - name: Build Xcode project
-        run: |
-          xcodebuild -downloadPlatform iOS
-          xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' FRAMEWORK_FOLDER_PATH=./build-ios build
-
-  macos-latest-tvos:
-    runs-on: macos-latest
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Build
-        id: cmake_build
-        run: |
-          sysctl -a
-          cmake -B build -G Xcode \
-            -DGGML_METAL_EMBED_LIBRARY=ON \
-            -DLLAMA_BUILD_COMMON=OFF \
-            -DLLAMA_BUILD_APP=OFF \
-            -DLLAMA_BUILD_EXAMPLES=OFF \
-            -DLLAMA_BUILD_TOOLS=OFF \
-            -DLLAMA_BUILD_TESTS=OFF \
-            -DLLAMA_BUILD_SERVER=OFF \
-            -DCMAKE_SYSTEM_NAME=tvOS \
-            -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
-            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
-          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
-
-  macos-latest-visionos:
-    runs-on: macos-latest
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Build
-        id: cmake_build
-        run: |
-          sysctl -a
-          cmake -B build -G Xcode \
-            -DGGML_METAL_EMBED_LIBRARY=ON \
-            -DLLAMA_BUILD_COMMON=OFF \
-            -DLLAMA_BUILD_APP=OFF \
-            -DLLAMA_BUILD_EXAMPLES=OFF \
-            -DLLAMA_BUILD_TOOLS=OFF \
-            -DLLAMA_BUILD_TESTS=OFF \
-            -DLLAMA_BUILD_SERVER=OFF \
-            -DCMAKE_SYSTEM_NAME=visionOS \
-            -DCMAKE_OSX_DEPLOYMENT_TARGET=1.0 \
-            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
-          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
-
-  macos-latest-swift:
-    runs-on: macos-latest
-    needs: macos-latest-ios-xcode
-
-    permissions:
-      actions: read
-      contents: read
-
-    strategy:
-      matrix:
-        destination: ['generic/platform=macOS', 'generic/platform=iOS', 'generic/platform=tvOS']
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Download xcframework artifact
-        uses: actions/download-artifact@v7
-        with:
-          name: llama-xcframework
-          path: build-apple/llama.xcframework/
-
-      - name: Build llama.cpp with CMake
-        id: cmake_build
-        run: |
-          sysctl -a
-          cmake -B build -G Xcode \
-            -DGGML_METAL_EMBED_LIBRARY=ON \
-            -DLLAMA_OPENSSL=OFF \
-            -DLLAMA_BUILD_APP=OFF \
-            -DLLAMA_BUILD_EXAMPLES=OFF \
-            -DLLAMA_BUILD_TOOLS=OFF \
-            -DLLAMA_BUILD_TESTS=OFF \
-            -DLLAMA_BUILD_SERVER=OFF \
-            -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64"
-          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
@@ -0,0 +1,37 @@
+name: "PR video demo label"
+
+on:
+  pull_request_target:
+    types: [opened, edited, reopened, synchronize]
+
+cache-mode: none
+permissions:
+  pull-requests: write
+  issues: write
+
+jobs:
+  video-demo-label:
+    runs-on: ubuntu-slim
+    steps:
+      - name: Add or remove the video demo label
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          PR_URL: ${{ github.event.pull_request.html_url }}
+          PR_NUMBER: ${{ github.event.pull_request.number }}
+          # passed through env, so the body is only matched, never run, and is safe to read from a fork
+          BODY: ${{ github.event.pull_request.body }}
+          HAS_LABEL: ${{ contains(github.event.pull_request.labels.*.name, 'video demo') }}
+        run: |
+          # sections written by demo:pr (see tools/ui/demo/cli.ts): the managed comment
+          # markers, or a preview linking to a demo video uploaded for this PR
+          has_demo=false
+          if { grep -qF '<!-- demo:start -->' <<< "$BODY" && grep -qF '<!-- demo:end -->' <<< "$BODY"; } ||
+            grep -qE "/resolve/videos/pr/$PR_NUMBER/[0-9a-f]{10}(-[0-9]+)?\.mp4" <<< "$BODY"; then
+            has_demo=true
+          fi
+
+          if [ "$has_demo" = true ] && [ "$HAS_LABEL" = false ]; then
+            gh pr edit "$PR_URL" --add-label 'video demo'
+          elif [ "$has_demo" = false ] && [ "$HAS_LABEL" = true ]; then
+            gh pr edit "$PR_URL" --remove-label 'video demo'
+          fi
@@ -150,6 +150,7 @@ option(LLAMA_LLGUIDANCE "llama-common: include LLGuidance library for structured
 # Required for relocatable CMake package
 include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake)
 include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/common.cmake)
+include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/win-app.cmake)

 if (NOT DEFINED LLAMA_BUILD_NUMBER)
    set(LLAMA_BUILD_NUMBER        ${BUILD_NUMBER})
@@ -269,6 +270,8 @@ if (LLAMA_BUILD_MTMD AND NOT (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TOOLS))
    add_subdirectory(tools/mtmd)
 endif()

+llama_apply_app_manifests()
+
 #
 # install
 #
@@ -13,7 +13,7 @@
 [![Docker](https://img.shields.io/github/actions/workflow/status/ggml-org/llama.cpp/docker.yml?label=Docker)](https://github.com/ggml-org/llama.cpp/actions/workflows/docker.yml)
 [![Winget](https://img.shields.io/github/actions/workflow/status/ggml-org/llama.cpp/winget.yml?label=Winget)](https://github.com/ggml-org/llama.cpp/actions/workflows/winget.yml)

-[ggml](https://github.com/ggml-org/ggml) / [ops](https://github.com/ggml-org/llama.cpp/blob/master/docs/ops.md) / [maintainer PRs](https://github.com/ggml-org/llama.cpp/issues?q=is%3Apr%20is%3Aopen%20draft%3AFalse%20(author%3Asanmai%20OR%20author%3Argerganov%20OR%20author%3AKitaitiMakoto%20OR%20author%3Adanbev%20OR%20author%3Aaldehir%20OR%20author%3Amax-krasnyansky%20OR%20author%3ACISC%20OR%20author%3Aggerganov%20OR%20author%3Aam17an%20OR%20author%3Ajhen0409%20OR%20author%3Abartowski1182%20OR%20author%3Anikwen%20OR%20author%3Ahipudding%20OR%20author%3Aravi9%20OR%20author%3AServeurpersoCom%20OR%20author%3Athelittlefireman%20OR%20author%3Apwilkin%20OR%20author%3Areeselevine%20OR%20author%3Angxson%20OR%20author%3Ajeffbolznv%20OR%20author%3Amarty1885%20OR%20author%3A0cc4m%20OR%20author%3ATitaniumtown%20OR%20author%3Aangt%20OR%20author%3AIMbackK%20OR%20author%3Aarthw%20OR%20author%3AJohannesGaessler%20OR%20author%3AORippler%20OR%20author%3Aruixiang63%20OR%20author%3Axctan%20OR%20author%3AmasterFoad%20OR%20author%3Aallozaur%20OR%20author%3AAndreas-Krebbel%20OR%20author%3Ayomaytk%20OR%20author%3Aaendk%20OR%20author%3Awine99%20OR%20author%3Agaugarg-nv%20OR%20author%3Ataronaeo%20OR%20author%3Aforforever73%20OR%20author%3Achaxu01%20OR%20author%3Alhez%20OR%20author%3AAlekseiNikiforovIBM%20OR%20author%3Anetrunnereve%20OR%20author%3Afairydreaming%20OR%20author%3Aynankani)%20sort%3Aupdated-desc) / [dev stats](https://github.com/ggml-org/llama.cpp-dev) / [lib llama API](https://github.com/ggml-org/llama.cpp/issues/9289) / [llama-server REST API](https://github.com/ggml-org/llama.cpp/issues/9291)
+[ggml](https://github.com/ggml-org/ggml) / [ops](https://github.com/ggml-org/llama.cpp/blob/master/docs/ops.md) / [maintainer PRs](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Apr%20is%3Aopen%20draft%3AFalse%20(author%3Asanmai%20OR%20author%3Argerganov%20OR%20author%3AKitaitiMakoto%20OR%20author%3Adanbev%20OR%20author%3Aaldehir%20OR%20author%3Amax-krasnyansky%20OR%20author%3ACISC%20OR%20author%3Aggerganov%20OR%20author%3Aam17an%20OR%20author%3Ajhen0409%20OR%20author%3Abartowski1182%20OR%20author%3Anikwen%20OR%20author%3Ahipudding%20OR%20author%3Aravi9%20OR%20author%3AServeurpersoCom%20OR%20author%3Athelittlefireman%20OR%20author%3Apwilkin%20OR%20author%3Areeselevine%20OR%20author%3Angxson%20OR%20author%3Ajeffbolznv%20OR%20author%3Amarty1885%20OR%20author%3A0cc4m%20OR%20author%3ATitaniumtown%20OR%20author%3Aangt%20OR%20author%3AIMbackK%20OR%20author%3Aarthw%20OR%20author%3AJohannesGaessler%20OR%20author%3AORippler%20OR%20author%3Aruixiang63%20OR%20author%3Axctan%20OR%20author%3AmasterFoad%20OR%20author%3Aallozaur%20OR%20author%3AAndreas-Krebbel%20OR%20author%3Ayomaytk%20OR%20author%3Aaendk%20OR%20author%3Awine99%20OR%20author%3Agaugarg-nv%20OR%20author%3Ataronaeo%20OR%20author%3Aforforever73%20OR%20author%3Achaxu01%20OR%20author%3Alhez%20OR%20author%3AAlekseiNikiforovIBM%20OR%20author%3Anetrunnereve%20OR%20author%3Afairydreaming%20OR%20author%3Aynankani)%20sort%3Aupdated-desc) / [dev stats](https://github.com/ggml-org/llama.cpp-dev) / [lib llama API](https://github.com/ggml-org/llama.cpp/issues/9289) / [llama-server REST API](https://github.com/ggml-org/llama.cpp/issues/9291)

 </div>

@@ -79,7 +79,7 @@ The `llama.cpp` project is build on top of the [ggml](https://github.com/ggml-or
 | --- | --- |
 | [BLAS](docs/build.md#blas-build) | All |
 | [BLIS](docs/backend/BLIS.md) | All |
-| [CANN](docs/build.md#cann) | Ascend NPU |
+| [CANN](docs/build.md#cann) | Huawei Ascend NPU |
 | [CUDA](docs/build.md#cuda) | Nvidia GPU |
 | [HIP](docs/build.md#hip) | AMD GPU |
 | [Hexagon](docs/backend/snapdragon/README.md) | Snapdragon |
@@ -0,0 +1,66 @@
+set(LLAMA_WIN_APP_MANIFEST "${CMAKE_CURRENT_LIST_DIR}/../common/win-app.manifest")
+
+if(WIN32)
+    if(MSVC OR CMAKE_CXX_SIMULATE_ID STREQUAL "MSVC")
+        set(LLAMA_WIN_APP_MSVC_ABI TRUE)
+    else()
+        set(LLAMA_WIN_APP_MSVC_ABI FALSE)
+    endif()
+
+    if(NOT LLAMA_WIN_APP_MSVC_ABI)
+        if(CMAKE_VERSION VERSION_LESS 3.21)
+            message(FATAL_ERROR "CMake 3.21 or newer is required to build for Windows with the GNU ABI")
+        endif()
+        enable_language(RC)
+        set(LLAMA_WIN_APP_MANIFEST_RC "${CMAKE_CURRENT_BINARY_DIR}/win-app-manifest.rc")
+        configure_file("${CMAKE_CURRENT_LIST_DIR}/win-app.rc.in" "${LLAMA_WIN_APP_MANIFEST_RC}" @ONLY)
+        set_property(DIRECTORY APPEND PROPERTY CMAKE_CONFIGURE_DEPENDS "${LLAMA_WIN_APP_MANIFEST}")
+    endif()
+endif()
+
+function(llama_add_app_manifest target)
+    if(NOT WIN32)
+        return()
+    endif()
+    get_target_property(_aliased ${target} ALIASED_TARGET)
+    if(_aliased)
+        return()
+    endif()
+    get_target_property(_type ${target} TYPE)
+    if(NOT _type STREQUAL "EXECUTABLE")
+        return()
+    endif()
+    if(LLAMA_WIN_APP_MSVC_ABI)
+        target_sources(${target} PRIVATE "${LLAMA_WIN_APP_MANIFEST}")
+    else()
+        if(NOT TARGET llama-win-app-manifest-res)
+            add_library(llama-win-app-manifest-res OBJECT ${LLAMA_WIN_APP_MANIFEST_RC})
+            set_source_files_properties(${LLAMA_WIN_APP_MANIFEST_RC} PROPERTIES OBJECT_DEPENDS "${LLAMA_WIN_APP_MANIFEST}")
+        endif()
+        target_link_libraries(${target} PRIVATE "$<TARGET_OBJECTS:llama-win-app-manifest-res>")
+    endif()
+endfunction()
+
+function(llama_apply_app_manifests_dir dir)
+    get_property(_targets DIRECTORY "${dir}" PROPERTY BUILDSYSTEM_TARGETS)
+    foreach(_target IN LISTS _targets)
+        llama_add_app_manifest(${_target})
+    endforeach()
+    get_property(_subdirs DIRECTORY "${dir}" PROPERTY SUBDIRECTORIES)
+    foreach(_subdir IN LISTS _subdirs)
+        llama_apply_app_manifests_dir("${_subdir}")
+    endforeach()
+endfunction()
+
+function(llama_apply_app_manifests)
+    if(NOT WIN32)
+        return()
+    endif()
+    get_property(_subdirs DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}" PROPERTY SUBDIRECTORIES)
+    foreach(_dir IN LISTS _subdirs)
+        get_filename_component(_name "${_dir}" NAME)
+        if(_name STREQUAL "app" OR _name STREQUAL "examples" OR _name STREQUAL "pocs" OR _name STREQUAL "tests" OR _name STREQUAL "tools")
+            llama_apply_app_manifests_dir("${_dir}")
+        endif()
+    endforeach()
+endfunction()
@@ -0,0 +1,3 @@
+// generated from cmake/win-app.rc.in - do not edit
+// RT_MANIFEST (24), ID 1: the application manifest shared by all llama.cpp executables
+1 24 "@LLAMA_WIN_APP_MANIFEST@"
@@ -12,16 +12,6 @@
 #include "speculative.h"
 #include "preset.h"

-// fix problem with std::min and std::max
-#if defined(_WIN32)
-#define WIN32_LEAN_AND_MEAN
-#ifndef NOMINMAX
-#   define NOMINMAX
-#endif
-#include <windows.h>
-#include <shellapi.h>
-#endif
-
 #include <algorithm>
 #include <cinttypes>
 #include <climits>
@@ -38,20 +28,6 @@
 #include <thread> // for hardware_concurrency
 #include <vector>

-#ifndef __EMSCRIPTEN__
-#ifdef __linux__
-#include <linux/limits.h>
-#elif defined(_WIN32)
-#   if !defined(PATH_MAX)
-#   define PATH_MAX MAX_PATH
-#   endif
-#elif defined(_AIX)
-#include <sys/limits.h>
-#else
-#include <sys/syslimits.h>
-#endif
-#endif
-
 #define LLAMA_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083

 using json = common_json;
@@ -351,7 +327,7 @@ static bool parse_bool_value(const std::string & value) {
 static std::string get_default_local_path(const std::string & url) {
    auto f = string_split<std::string>(url, '#').front();
    f = string_split<std::string>(f, '?').front();
-    return fs_path_to_utf8(fs_get_cache_file(string_split<std::string>(f, '/').back()));
+    return fs_get_cache_file(string_split<std::string>(f, '/').back()).string();
 }

 static bool spec_types_is_default(const common_params & params) {
@@ -762,7 +738,7 @@ static void common_params_apply_system_config(common_params & params, llama_exam
    common_preset_context ctx(ex);
    ctx.ignore_unknown_keys = true; // the same config file is shared by all programs
    for (const auto & path : found) {
-        LOG_INF("using config file: %s\n", fs_path_to_utf8(path).c_str());
+        LOG_INF("using config file: %s\n", path.string().c_str());
        common_preset global;
        common_presets presets = ctx.load_from_ini(path, global);
        global.apply_to_params(params);
@@ -1259,44 +1235,7 @@ bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<com
    return true;
 }

-#ifdef _WIN32
-struct utf8_argv {
-    std::vector<std::string> buf;
-    std::vector<char*> ptrs;
-};
-
-static utf8_argv make_utf8_argv() {
-    utf8_argv out;
-    int wargc = 0;
-    LPWSTR* wargv = CommandLineToArgvW(GetCommandLineW(), &wargc);
-    if (!wargv) return out;
-
-    out.buf.reserve(wargc);
-    for (int i = 0; i < wargc; ++i) {
-        int n = WideCharToMultiByte(CP_UTF8, WC_ERR_INVALID_CHARS, wargv[i], -1, nullptr, 0, nullptr, nullptr);
-        if (n <= 0) { out.buf.emplace_back(); continue; }
-        auto& s = out.buf.emplace_back();
-        s.resize(static_cast<size_t>(n - 1));
-        (void)WideCharToMultiByte(CP_UTF8, 0, wargv[i], -1, s.data(), n, nullptr, nullptr);
-    }
-    LocalFree(wargv);
-
-    out.ptrs.reserve(out.buf.size() + 1);
-    for (auto& s : out.buf) out.ptrs.push_back(s.data());
-    out.ptrs.push_back(nullptr);
-    return out;
-}
-#endif
-
 bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
-#ifdef _WIN32
-    auto utf8 = make_utf8_argv();
-    // repair argv only when it matches the process command line
-    if (static_cast<int>(utf8.buf.size()) == argc) {
-        argv = utf8.ptrs.data();
-    }
-#endif
-
    auto ctx_arg = common_params_parser_init(params, ex, print_usage);
    const common_params params_org = ctx_arg.params; // the example can modify the default params

@@ -219,6 +219,23 @@ int32_t common_cpu_get_num_math() {
            }
        }
    }
+#elif defined(__riscv) && defined(__linux__)
+    std::ifstream cpuinfo("/proc/cpuinfo");
+    std::string line;
+    int32_t n_a100 = 0;
+    while (std::getline(cpuinfo, line)) {
+        std::istringstream fields(line);
+        std::string key;
+        std::string separator;
+        uint64_t marchid;
+        if (fields >> key >> separator >> std::hex >> marchid &&
+            key == "marchid" && separator == ":" && marchid == UINT64_C(0x8000000041000002)) {
+            ++n_a100;
+        }
+    }
+    if (n_a100 > 0) {
+        return n_a100;
+    }
 #elif defined(__powerpc64__) || defined(__powerpc__)
    int32_t smt_factor = 1;
    int phy_cpus = common_cpu_get_num_physical_cores();
@@ -917,12 +934,6 @@ std::string wstring_to_utf8(const std::wstring & str) {
 }
 #endif

-// returns the path as a UTF-8 string, preserving its separators
-std::string fs_path_to_utf8(const std::filesystem::path & path) {
-    const auto value = path.u8string();
-    return std::string(value.begin(), value.end());
-}
-
 void fs_write_atomic(const std::filesystem::path & path, const std::string & data) {
    // a random suffix gives every writer its own temporary file, across processes and containers
    std::random_device rd;
@@ -946,7 +957,7 @@ void fs_write_atomic(const std::filesystem::path & path, const std::string & dat

    if (file.fail() || ec) {
        std::filesystem::remove(path_tmp, ec);
-        throw std::runtime_error("failed to write file: " + fs_path_to_utf8(path));
+        throw std::runtime_error("failed to write file: " + path.string());
    }
 }

@@ -1040,7 +1051,7 @@ std::filesystem::path fs_get_cache_file(const std::string & filename) {
    std::error_code ec;
    common_create_directories(cache_directory, ec);
    if (ec) {
-        throw std::runtime_error("failed to create cache directory: " + fs_path_to_utf8(cache_directory));
+        throw std::runtime_error("failed to create cache directory: " + cache_directory.string());
    }
    return cache_directory / std::filesystem::u8path(filename);
 }
@@ -2264,6 +2275,16 @@ int32_t common_batch::add_embd(llama_embd embd, const llama_pos * pos, llama_seq
    return size() - 1;
 }

+int32_t common_batch::add_embd_row(int32_t row, const llama_pos * pos, llama_seq_id seq_id, bool output) {
+    token t = { LLAMA_TOKEN_NULL, { 0, 0, 0, 0 }, seq_id, output, { nullptr, 0, 0 }, { nullptr, 0, 0 }, {} };
+    t.embd_row = row;
+    for (int32_t j = 0; j < n_pos; ++j) {
+        t.pos[j] = pos[j];
+    }
+    tokens.push_back(t);
+    return size() - 1;
+}
+
 llama_batch_ext * common_batch::get_sub_batch(int32_t off, int32_t n) {
    GGML_ASSERT(batch && "common_batch was not initialized with a context");
    GGML_ASSERT(off >= 0 && n >= 0 && off + n <= size());
@@ -2284,6 +2305,12 @@ llama_batch_ext * common_batch::get_sub_batch(int32_t off, int32_t n) {
            if (t.embd.data && !llama_batch_ext_set_embd_token(res, idx, t.embd)) {
                GGML_ABORT("%s: failed to set the embedding of token %d at index %d\n", __func__, t.id, i);
            }
+        } else if (t.embd_row >= 0) {
+            idx = llama_batch_ext_add_embd_row(res, t.seq_id, t.embd_row);
+            if (idx < 0) {
+                GGML_ABORT("%s: failed to add embd row %d at index %d (error %d, n = %d)\n", __func__, t.embd_row, i, idx, n);
+            }
+            llama_batch_ext_set_pos(res, idx, t.pos.data());
        } else {
            idx = llama_batch_ext_add_embd(res, t.seq_id, t.embd);
            if (idx < 0) {
@@ -910,9 +910,6 @@ std::wstring utf8_to_wstring(const std::string & str);
 std::string  wstring_to_utf8(const std::wstring & str);
 #endif

-// returns the path as a UTF-8 string, preserving its separators
-std::string fs_path_to_utf8(const std::filesystem::path & path);
-
 //
 // Environment utils
 //
@@ -1099,6 +1096,7 @@ struct common_batch {
        llama_embd   state; // non-owning view of the data passed to set_embd_state(), data == NULL if none
        std::vector<llama_seq_id> seq_ids_extra; // see add_seq()
        int32_t      decision_order = 0; // see llama_batch_ext_set_decision_order()
+        int32_t      embd_row = -1; // see add_embd_row(), -1 if none
    };

    std::vector<token> tokens; // mirror of the entries, tokens[i] describes batch index i
@@ -1141,6 +1139,10 @@ struct common_batch {
    // pos points to n_pos positions
    int32_t add_embd(llama_embd embd, const llama_pos * pos, llama_seq_id seq_id, bool output);

+    // add an entry whose embedding is row `row` of the layer input device buffer of the context's ctx_other
+    // pos points to n_pos positions
+    int32_t add_embd_row(int32_t row, const llama_pos * pos, llama_seq_id seq_id, bool output);
+
    int32_t size() const { return (int32_t) tokens.size(); }
 };

@@ -748,7 +748,8 @@ static hf_cache::hf_file find_best_model(const hf_cache::hf_files & files,
    }

    for (const auto & t : tags) {
-        std::regex pattern(t + "[.-]", std::regex::icase);
+        // match the tag as a full token, so ":F16" does not match a BF16 file
+        std::regex pattern("(^|[-./])" + t + "[.-]", std::regex::icase);
        for (const auto & f : files) {
            if (gguf_filename_is_model(f.path) &&
                std::regex_search(f.path, pattern)) {
@@ -1005,7 +1006,7 @@ std::string common_docker_resolve_model(const std::string & docker) {
        std::string model_filename = repo;
        std::replace(model_filename.begin(), model_filename.end(), '/', '_');
        model_filename += "_" + tag + ".gguf";
-        std::string local_path = fs_path_to_utf8(fs_get_cache_file(model_filename));
+        std::string local_path = fs_get_cache_file(model_filename).string();

        const std::string blob_url = url_prefix + "/blobs/" + gguf_digest;
        common_download_opts opts;
@@ -63,7 +63,7 @@ static fs::path get_cache_directory() {
 }

 std::string get_cache_path() {
-    return fs_path_to_utf8(get_cache_directory());
+    return get_cache_directory().string();
 }

 static std::string folder_name_to_repo(const std::string & folder) {
@@ -340,11 +340,11 @@ hf_files get_repo_files(const std::string & repo_id,
            file.url = endpoint + repo_id + "/resolve/" + commit + "/" + file.path;

            fs::path final_path = commit_path / subpath;
-            file.final_path = fs_path_to_utf8(final_path);
+            file.final_path = final_path.string();

            if (!file.oid.empty() && !fs::exists(final_path)) {
                fs::path local_path = blobs_path / file.oid;
-                file.local_path = fs_path_to_utf8(local_path);
+                file.local_path = local_path.string();
            } else {
                file.local_path = file.final_path;
            }
@@ -411,7 +411,7 @@ hf_files get_cached_files(const std::string & repo_id) {
        if (!fs::exists(snapshots_path)) {
            continue;
        }
-        std::string _repo_id = folder_name_to_repo(fs_path_to_utf8(repo.path().filename()));
+        std::string _repo_id = folder_name_to_repo(repo.path().filename().string());

        if (!is_valid_repo_id(_repo_id)) {
            continue;
@@ -436,7 +436,7 @@ hf_files get_cached_files(const std::string & repo_id) {
                file.repo_id = _repo_id;
                const auto generic_path = path.generic_u8string();
                file.path = std::string(generic_path.begin(), generic_path.end());
-                file.local_path = fs_path_to_utf8(entry.path());
+                file.local_path = entry.path().string();
                file.final_path = file.local_path;
                files.push_back(std::move(file));
            }
@@ -516,7 +516,7 @@ bool remove_cached_repo(const std::string & repo_id) {
    std::error_code ec;
    auto removed = fs::remove_all(repo_path, ec);
    if (ec) {
-        LOG_ERR("%s: failed to remove repo cache %s: %s\n", __func__, fs_path_to_utf8(repo_path).c_str(), ec.message().c_str());
+        LOG_ERR("%s: failed to remove repo cache %s: %s\n", __func__, repo_path.string().c_str(), ec.message().c_str());
        return false;
    }
    return removed > 0;
@@ -307,58 +307,13 @@ value binary_expression::execute_impl(context & ctx) const {
    throw std::runtime_error("Unknown operator \"" + op.value + "\" between " + left_val->type() + " and " + right_val->type());
 }

-static value try_builtin_func(context & ctx, const std::string & name, value & input, bool undef_on_missing = false) {
-    JJ_DEBUG("Trying built-in function '%s' for type %s", name.c_str(), input->type().c_str());
-    if (ctx.is_get_stats) {
-        value_t::stats_t::mark_used(input);
-        input->stats.ops.insert(name);
-    }
-    auto builtins = input->get_builtins();
-    auto it = builtins.find(name);
-    if (it != builtins.end()) {
-        JJ_DEBUG("Binding built-in '%s'", name.c_str());
-        return mk_val<value_func>(name, it->second, input);
-    }
-    if (undef_on_missing) {
-        return mk_val<value_undefined>(name);
-    }
-    throw std::runtime_error("Unknown (built-in) filter '" + name + "' for type " + input->type());
-}
-
 static value apply_filter(context & ctx, const statement_ptr & filter, value input) {
    JJ_DEBUG("Applying filter to %s", input->type().c_str());

-    auto set_filter_alias = [](auto & filter_id) {
-        if (filter_id == "count") {
-            filter_id = "length";
-        } else if (filter_id == "d") {
-            filter_id = "default";
-        } else if (filter_id == "e") {
-            filter_id = "escape";
-        } else if (filter_id == "trim") {
-            filter_id = "strip";
-        }
-    };
-
    if (is_stmt<identifier>(filter)) {
        auto filter_id = cast_stmt<identifier>(filter)->val;
-
-        set_filter_alias(filter_id);
        JJ_DEBUG("Applying filter '%s' to %s", filter_id.c_str(), input->type().c_str());
-        // TODO: Refactor filters so this coercion can be done automatically
-        if (!input->is_undefined() && !is_val<value_string>(input) && (
-            filter_id == "capitalize" ||
-            filter_id == "lower" ||
-            filter_id == "replace" ||
-            filter_id == "strip" ||
-            filter_id == "title" ||
-            filter_id == "upper" ||
-            filter_id == "wordcount"
-        )) {
-            JJ_DEBUG("Coercing %s to String for '%s' filter", input->type().c_str(), filter_id.c_str());
-            input = mk_val<value_string>(input->as_string());
-        }
-        return try_builtin_func(ctx, filter_id, input)->invoke(func_args(ctx));
+        return apply_filter_by_name(ctx, filter_id, input, func_args(ctx));

    } else if (is_stmt<call_expression>(filter)) {
        auto call = cast_stmt<call_expression>(filter);
@@ -367,14 +322,13 @@ static value apply_filter(context & ctx, const statement_ptr & filter, value inp
        }
        auto filter_id = cast_stmt<identifier>(call->callee)->val;

-        set_filter_alias(filter_id);
        JJ_DEBUG("Applying filter '%s' with arguments to %s", filter_id.c_str(), input->type().c_str());
        func_args args(ctx);
        for (const auto & arg_expr : call->args) {
            args.push_back(arg_expr->execute(ctx));
        }

-        return try_builtin_func(ctx, filter_id, input)->invoke(args);
+        return apply_filter_by_name(ctx, filter_id, input, args);

    } else {
        throw std::runtime_error("Invalid filter expression");
@@ -72,6 +72,17 @@ const std::vector<value> & func_args::get_args() const {
    return args;
 }

+void func_args::ensure_kwargs(std::initializer_list<const char *> names) const {
+    for (const auto & arg : args) {
+        if (is_val<value_kwarg>(arg)) {
+            auto * kwarg = cast_val<value_kwarg>(arg);
+            if (std::find(names.begin(), names.end(), kwarg->key) == names.end()) {
+                throw raised_exception("Function '" + func_name + "' got an unexpected keyword argument '" + kwarg->key + "'");
+            }
+        }
+    }
+}
+
 /**
 * Function that mimics Python's array slicing.
 */
@@ -134,6 +145,14 @@ static value empty_value_fn(const func_args &) {
        return mk_val<T>();
    }
 }
+
+template<typename T>
+static value empty_value_no_args_fn(const func_args & args) {
+    args.ensure_kwargs();
+    args.ensure_count(1, 1);
+    return empty_value_fn<T>(args);
+}
+
 template<typename T>
 static value test_type_fn(const func_args & args) {
    args.ensure_count(1);
@@ -594,6 +613,52 @@ const func_builtins & global_builtins() {
    return builtins;
 }

+value try_builtin_func(context & ctx, const std::string & name, value & input, bool undef_on_missing) {
+    JJ_DEBUG("Trying built-in function '%s' for type %s", name.c_str(), input->type().c_str());
+    if (ctx.is_get_stats) {
+        value_t::stats_t::mark_used(input);
+        input->stats.ops.insert(name);
+    }
+    auto builtins = input->get_builtins();
+    auto it = builtins.find(name);
+    if (it != builtins.end()) {
+        JJ_DEBUG("Binding built-in '%s'", name.c_str());
+        return mk_val<value_func>(name, it->second, input);
+    }
+    if (undef_on_missing) {
+        return mk_val<value_undefined>(name);
+    }
+    throw std::runtime_error("Unknown (built-in) filter '" + name + "' for type " + input->type());
+}
+
+value apply_filter_by_name(context & ctx, std::string filter_id, value input, const func_args & args) {
+    if (filter_id == "count") {
+        filter_id = "length";
+    } else if (filter_id == "d") {
+        filter_id = "default";
+    } else if (filter_id == "e") {
+        filter_id = "escape";
+    } else if (filter_id == "trim") {
+        filter_id = "strip";
+    }
+
+    // TODO: Refactor filters so this coercion can be done automatically
+    if (!input->is_undefined() && !is_val<value_string>(input) && (
+        filter_id == "capitalize" ||
+        filter_id == "lower" ||
+        filter_id == "replace" ||
+        filter_id == "strip" ||
+        filter_id == "title" ||
+        filter_id == "upper" ||
+        filter_id == "wordcount"
+    )) {
+        JJ_DEBUG("Coercing %s to String for '%s' filter", input->type().c_str(), filter_id.c_str());
+        input = mk_val<value_string>(input->as_string());
+    }
+
+    return try_builtin_func(ctx, filter_id, input)->invoke(args);
+}
+

 const func_builtins & value_int_t::get_builtins() const {
    static const func_builtins builtins = {
@@ -667,16 +732,22 @@ const func_builtins & value_string_t::get_builtins() const {
    static const func_builtins builtins = {
        {"default", default_value},
        {"upper", [](const func_args & args) -> value {
+            args.ensure_kwargs();
+            args.ensure_count(1, 1);
            args.ensure_vals<value_string>();
            jinja::string str = args.get_pos(0)->as_string().uppercase();
            return mk_val<value_string>(str);
        }},
        {"lower", [](const func_args & args) -> value {
+            args.ensure_kwargs();
+            args.ensure_count(1, 1);
            args.ensure_vals<value_string>();
            jinja::string str = args.get_pos(0)->as_string().lowercase();
            return mk_val<value_string>(str);
        }},
        {"strip", [](const func_args & args) -> value {
+            args.ensure_kwargs({"chars"});
+            args.ensure_count(1, 2);
            value val_input = args.get_pos(0);
            if (!is_val<value_string>(val_input)) {
                throw raised_exception("strip() first argument must be a string");
@@ -707,11 +778,15 @@ const func_builtins & value_string_t::get_builtins() const {
            }
        }},
        {"title", [](const func_args & args) -> value {
+            args.ensure_kwargs();
+            args.ensure_count(1, 1);
            args.ensure_vals<value_string>();
            jinja::string str = args.get_pos(0)->as_string().titlecase();
            return mk_val<value_string>(str);
        }},
        {"capitalize", [](const func_args & args) -> value {
+            args.ensure_kwargs();
+            args.ensure_count(1, 1);
            args.ensure_vals<value_string>();
            jinja::string str = args.get_pos(0)->as_string().capitalize();
            return mk_val<value_string>(str);
@@ -1013,6 +1088,10 @@ const func_builtins & value_bool_t::get_builtins() const {
    return builtins;
 }

+[[noreturn]] static value array_sum_not_implemented(const func_args &) {
+    throw not_implemented_exception("Array sum builtin not implemented");
+}
+
 const func_builtins & value_array_t::get_builtins() const {
    static const func_builtins builtins = {
        {"default", default_value},
@@ -1079,7 +1158,9 @@ const func_builtins & value_array_t::get_builtins() const {
        {"select", select_or_reject<false, false>},
        {"rejectattr", select_or_reject<true, true>},
        {"reject", select_or_reject<true, false>},
+        {"sum", array_sum_not_implemented},
        {"join", [](const func_args & args) -> value {
+            args.ensure_kwargs({"d", "attribute"});
            args.ensure_count(1, 3);
            if (!is_val<value_array>(args.get_pos(0))) {
                throw raised_exception("join() first argument must be an array");
@@ -1117,13 +1198,28 @@ const func_builtins & value_array_t::get_builtins() const {
        {"tojson", tojson},
        {"map", [](const func_args & args) -> value {
            args.ensure_count(2);
-            if (!is_val<value_array>(args.get_pos(0))) {
+            value val = args.get_pos(0);
+            if (!is_val<value_array>(val)) {
                throw raised_exception("map: first argument must be an array");
            }
            if (!is_val<value_kwarg>(args.get_args().at(1))) {
-                throw not_implemented_exception("map: filter-mapping not implemented");
+                value filter = args.get_pos(1);
+                if (!is_val<value_string>(filter)) {
+                    throw raised_exception("map: filter name must be a string");
+                }
+                auto out = mk_val<value_array>();
+                func_args filter_args(args.ctx);
+                const auto & all_args = args.get_args();
+                for (size_t i = 2; i < all_args.size(); ++i) {
+                    filter_args.push_back(all_args[i]);
+                }
+                const std::string filter_id = filter->as_string().str();
+                auto arr = val->as_array();
+                for (const auto & item : arr) {
+                    out->push_back(apply_filter_by_name(args.ctx, filter_id, item, filter_args));
+                }
+                return is_val<value_tuple>(val) ? mk_val<value_tuple>(out->as_array()) : out;
            }
-            value val       = args.get_pos(0);
            value attribute = args.get_kwarg_or_pos("attribute", 1);
            value default_val = args.get_kwarg("default", mk_val<value_undefined>());
            auto out = mk_val<value_array>();
@@ -1132,7 +1228,7 @@ const func_builtins & value_array_t::get_builtins() const {
                value attr_val = get_attribute(item, attribute, default_val);
                out->push_back(attr_val);
            }
-            return is_val<value_tuple>(val) ? mk_val<value_tuple>(std::move(out->as_array())) : out;
+            return is_val<value_tuple>(val) ? mk_val<value_tuple>(out->as_array()) : out;
        }},
        {"append", [](const func_args & args) -> value {
            args.ensure_count(2);
@@ -1405,14 +1501,18 @@ const func_builtins & value_none_t::get_builtins() const {
 const func_builtins & value_undefined_t::get_builtins() const {
    static const func_builtins builtins = {
        {"default", default_value},
-        {"capitalize", empty_value_fn<value_string>},
+        {"capitalize", empty_value_no_args_fn<value_string>},
        {"first", empty_value_fn<value_undefined>},
        {"items", empty_value_fn<value_array>},
-        {"join", empty_value_fn<value_string>},
+        {"join", [](const func_args & args) -> value {
+            args.ensure_kwargs({"d", "attribute"});
+            args.ensure_count(1, 3);
+            return empty_value_fn<value_string>(args);
+        }},
        {"last", empty_value_fn<value_undefined>},
        {"length", empty_value_fn<value_int>},
        {"list", empty_value_fn<value_array>},
-        {"lower", empty_value_fn<value_string>},
+        {"lower", empty_value_no_args_fn<value_string>},
        {"map", empty_value_fn<value_array>},
        {"max", empty_value_fn<value_undefined>},
        {"min", empty_value_fn<value_undefined>},
@@ -1425,12 +1525,16 @@ const func_builtins & value_undefined_t::get_builtins() const {
        {"selectattr", empty_value_fn<value_array>},
        {"sort", empty_value_fn<value_array>},
        {"string", empty_value_fn<value_string>},
-        {"strip", empty_value_fn<value_string>},
+        {"strip", [](const func_args & args) -> value {
+            args.ensure_kwargs({"chars"});
+            args.ensure_count(1, 2);
+            return empty_value_fn<value_string>(args);
+        }},
        {"sum", empty_value_fn<value_int>},
-        {"title", empty_value_fn<value_string>},
+        {"title", empty_value_no_args_fn<value_string>},
        {"truncate", empty_value_fn<value_string>},
        {"unique", empty_value_fn<value_array>},
-        {"upper", empty_value_fn<value_string>},
+        {"upper", empty_value_no_args_fn<value_string>},
        {"wordcount", empty_value_fn<value_int>},
    };
    return builtins;
@@ -7,6 +7,7 @@
 #include <cmath>
 #include <cstdint>
 #include <functional>
+#include <initializer_list>
 #include <map>
 #include <memory>
 #include <set>
@@ -176,6 +177,8 @@ protected:
 //

 const func_builtins & global_builtins();
+value try_builtin_func(context & ctx, const std::string & name, value & input, bool undef_on_missing = false);
+value apply_filter_by_name(context & ctx, std::string filter_id, value input, const func_args & args);

 std::string value_to_json(const value & val, int indent = -1, const std::string_view item_sep = ", ", const std::string_view key_sep = ": ");

@@ -658,6 +661,7 @@ public:
    size_t count() const { return args.size(); }
    void push_back(const value & val);
    void push_front(const value & val);
+    void ensure_kwargs(std::initializer_list<const char *> names = {}) const;
    void ensure_count(size_t min, size_t max = 999) const {
        size_t n = args.size();
        if (n < min || n > max) {
@@ -71,6 +71,7 @@ common_chat_params common_chat_params_init_gpt_oss(const common_chat_template &
        auto content         = p.rule("message-content", p.until("<|end|>"));
        auto channel         = p.literal("<|channel|>") + (p.literal("commentary") | p.literal("analysis"));
        auto constrain_type  = p.chars("[A-Za-z0-9_-]", 1, -1);
+        auto constraint      = p.optional(p.space() + p.optional(p.literal("<|constrain|>")) + constrain_type);

        // Occasionally, gpt-oss-20b will prefix channels with this commentary
        auto stray_commentary = p.optional(p.literal("<|channel|>commentary") + p.optional(p.literal(" to=assistant")));
@@ -84,7 +85,7 @@ common_chat_params common_chat_params_init_gpt_oss(const common_chat_template &

        auto analysis = p.ref("analysis");
        auto preamble = p.rule("preamble", p.literal("<|channel|>commentary<|message|>") + p.content(content) + end);
-        auto final_msg = p.rule("final", stray_commentary + p.literal("<|channel|>final<|message|>") + p.content(content));
+        auto final_msg = p.rule("final", stray_commentary + p.literal("<|channel|>final") + constraint + p.literal("<|message|>") + p.content(content));

        // Consume any unsolicited tool calls, e.g. builtin functions
        auto unsolicited = p.rule("unsolicited", p.atomic(p.optional(channel) + p.literal(" to=") + content + end));
@@ -92,7 +93,6 @@ common_chat_params common_chat_params_init_gpt_oss(const common_chat_template &
        auto any = p.rule("any", preamble | analysis);

        if (has_response_format) {
-            auto constraint = p.optional(p.space() + p.optional(p.literal("<|constrain|>")) + constrain_type);
            auto response_format = p.rule("response-format",
                p.literal("<|channel|>final") + constraint + p.literal("<|message|>") +
                p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)));
@@ -109,7 +109,6 @@ common_chat_params common_chat_params_init_gpt_oss(const common_chat_template &
                const auto   params   = common_chat_tool_parameters(function);

                auto func_name  = p.literal(" to=functions.") + p.tool_name(p.literal(name));
-                auto constraint = p.optional(p.space() + p.optional(p.literal("<|constrain|>")) + constrain_type);
                auto args       = p.tool_args(p.schema(p.json(), "tool-" + std::to_string(tool_index) + "-schema", params));

                // recipient in role header
@@ -74,16 +74,24 @@ common_chat_params common_chat_params_init_qwen3_coder(const common_chat_templat
    data.parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
        auto generation_prompt = p.literal(GEN_PREFIX);

+        // The template strips the newline before </think>, so leave it out of the reasoning
+        auto think_body  = p.until_one_of({ "\n</think>", "</think>", "<tool_call>" });
+        auto think_close = p.literal("\n</think>") | p.literal("</think>") | p.peek(p.literal("<tool_call>"));
+
+        auto reasoning_extracted = p.optional("<think>" + p.space() + p.reasoning(think_body) + think_close);
+        auto reasoning_skipped   = p.optional("<think>" + p.space() + think_body + think_close);
+
        auto reasoning = p.eps();
        if (supports_reasoning && extract_reasoning) {
-            // The template strips the newline before </think>, so leave it out of the reasoning
-            reasoning = p.optional("<think>" + p.space() +
-                                   p.reasoning(p.until_one_of({ "\n</think>", "</think>", "<tool_call>" })) +
-                                   (p.literal("\n</think>") | p.literal("</think>") | p.peek(p.literal("<tool_call>"))));
+            reasoning = reasoning_extracted;
        }

        // Response format parser
        if (has_response_format) {
+            // The answer must be bare JSON, so the think block is consumed even when it is not extracted
+            if (supports_reasoning && !extract_reasoning) {
+                reasoning = reasoning_skipped;
+            }
            return generation_prompt + (reasoning << p.content(p.schema(p.json(), "response-format", inputs.json_schema)));
        }

@@ -171,12 +171,12 @@ static std::map<std::string, std::map<std::string, std::string>> parse_ini_from_
    std::map<std::string, std::map<std::string, std::string>> parsed;

    if (!std::filesystem::exists(path)) {
-        throw std::runtime_error("preset file does not exist: " + fs_path_to_utf8(path));
+        throw std::runtime_error("preset file does not exist: " + path.string());
    }

    std::ifstream file(path);
    if (!file.good()) {
-        throw std::runtime_error("failed to open server preset file: " + fs_path_to_utf8(path));
+        throw std::runtime_error("failed to open server preset file: " + path.string());
    }

    std::string contents((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
@@ -225,7 +225,7 @@ static std::map<std::string, std::map<std::string, std::string>> parse_ini_from_
    common_peg_parse_context ctx(contents);
    const auto result = parser.parse(ctx);
    if (!result.success()) {
-        throw std::runtime_error("failed to parse server config file: " + fs_path_to_utf8(path));
+        throw std::runtime_error("failed to parse server config file: " + path.string());
    }

    std::string current_section = COMMON_PRESET_DEFAULT_NAME;
@@ -323,7 +323,7 @@ common_presets common_preset_context::load_from_ini(const std::filesystem::path
                }
                LOG_DBG("accepted option: %s = %s\n", key.c_str(), preset.options[opt].c_str());
            } else if (ignore_unknown_keys) {
-                LOG_WRN("ignoring option '%s' from %s: not supported by this program\n", key.c_str(), fs_path_to_utf8(path).c_str());
+                LOG_WRN("ignoring option '%s' from %s: not supported by this program\n", key.c_str(), path.string().c_str());
            } else {
                throw std::runtime_error(string_format(
                    "option '%s' not recognized in preset '%s'",
@@ -401,7 +401,7 @@ common_presets common_preset_context::load_from_models_dir(const std::string & m
            if (!entry.is_regular_file(ec)) {
                continue;
            }
-            const std::string fname = fs_path_to_utf8(entry.path().filename());
+            const std::string fname = entry.path().filename().string();
            if (!string_ends_with(fname, ".gguf")) {
                continue;
            }
@@ -422,9 +422,9 @@ common_presets common_preset_context::load_from_models_dir(const std::string & m
        if (!path.empty()) {
            models.push_back({
                /* name        */ name,
-                /* path        */ fs_path_to_utf8(path),
-                /* path_mmproj */ fs_path_to_utf8(mmproj_file), // can be empty
-                /* path_draft  */ fs_path_to_utf8(draft_file)    // can be empty
+                /* path        */ path.string(),
+                /* path_mmproj */ mmproj_file.string(), // can be empty
+                /* path_draft  */ draft_file.string()   // can be empty
            });
        }
    };
@@ -432,13 +432,13 @@ common_presets common_preset_context::load_from_models_dir(const std::string & m
    for (const auto & entry : std::filesystem::directory_iterator(dir)) {
        std::error_code ec;
        if (entry.is_directory(ec)) {
-            scan_subdir(entry.path(), fs_path_to_utf8(entry.path().filename()));
+            scan_subdir(entry.path(), entry.path().filename().string());
            continue;
        }
        if (!entry.is_regular_file(ec)) {
            continue;
        }
-        const std::string fname = fs_path_to_utf8(entry.path().filename());
+        const std::string fname = entry.path().filename().string();
        if (!string_ends_with(fname, ".gguf")) {
            continue;
        }
@@ -450,7 +450,7 @@ common_presets common_preset_context::load_from_models_dir(const std::string & m
        string_replace_all(name, ".gguf", "");
        models.push_back({
            /* name        */ name,
-            /* path        */ fs_path_to_utf8(entry.path()),
+            /* path        */ entry.path().string(),
            /* path_mmproj */ "",
            /* path_draft  */ ""
        });
@@ -136,7 +136,8 @@ static void common_reasoning_budget_accept(struct llama_sampler * smpl, llama_to
            // track the end sequence within forced_tokens so it is also reported on DONE
            const int32_t match = ctx->end_matcher.advance(token);
            ctx->force_pos++;
-            if (ctx->force_pos >= ctx->forced_tokens.size()) {
+            // an end tag that is already in the prompt (e.g. "<think></think>") also ends forcing
+            if (match >= 0 || ctx->force_pos >= ctx->forced_tokens.size()) {
                ctx->state = REASONING_BUDGET_DONE;
                ctx->end_match = match;
                COM_TRC("%s", "forced sequence complete, done\n");
@@ -1027,6 +1027,9 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
    const int32_t * target_layer_ids   = nullptr; // model_dft's extract layer indices
    uint32_t        target_layer_ids_n = 0;

+    // inject target features from the target's device buffer instead of host copies (env LLAMA_DFLASH_DEV=0 disables)
+    bool use_dev_features = false;
+
    common_speculative_impl_draft_dflash(const common_params_speculative & params, uint32_t n_seq,
            common_speculative_type type = COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH)
        : common_speculative_impl(type, n_seq, params.draft.n_max)
@@ -1141,6 +1144,23 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
            llama_set_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k], true);
        }

+        // the device buffer keeps layers in ascending id order, the fc input expects target_layer_ids order
+        {
+            const char * env = getenv("LLAMA_DFLASH_DEV");
+            const bool ascending = std::adjacent_find(target_layer_ids, target_layer_ids + target_layer_ids_n,
+                    [](int32_t a, int32_t b) { return a >= b; }) == target_layer_ids + target_layer_ids_n;
+
+            use_dev_features = !is_lilicorr && ascending && (env == nullptr || atoi(env) != 0);
+            if (use_dev_features) {
+                llama_set_embeddings_layer_inp_dev(ctx_tgt, true);
+                use_dev_features = llama_supports_embd_row(ctx_dft);
+                if (!use_dev_features) {
+                    llama_set_embeddings_layer_inp_dev(ctx_tgt, false);
+                }
+            }
+            LOG_INF("%s: DFlash target features via %s\n", __func__, use_dev_features ? "device buffer" : "host copies");
+        }
+
        // DFlash2 reads its selector lattice from h_nextn and never consumes raw logits.
        llama_set_embeddings_nextn(ctx_dft, true, /*masked*/ !is_dflash2 && !is_lilicorr);
        llama_set_causal_attn(ctx_dft, causal_attn); // DFlash needs non-causal attention unless the model says otherwise
@@ -1233,6 +1253,25 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
            for (int32_t offset = 0; offset < n_rows; offset += n_ubatch) {
                const int32_t n_chunk = std::min(n_ubatch, n_rows - offset);

+                if (use_dev_features) {
+                    // the features are the target batch rows in the target's device buffer
+                    batch_inject.clear();
+                    for (int32_t i = 0; i < n_chunk; ++i) {
+                        const int32_t   row        = i_batch_beg[seq_id] + offset + i;
+                        const llama_pos p          = batch_in.tokens[row].pos[0];
+                        const llama_pos pos_arr[4] = { p, p, p, 0 };
+                        batch_inject.add_embd_row(row, pos_arr, seq_id, false);
+                    }
+
+                    const int32_t rc = llama_process(ctx_dft, LLAMA_PROCESS_TYPE_DECODE, batch_inject.get());
+                    if (rc != 0) {
+                        LOG_ERR("%s: llama_process(ctx_dft) failed rc=%d (n_tokens=%d, offset=%d)\n",
+                                __func__, rc, (int) n_chunk, (int) offset);
+                        return false;
+                    }
+                    continue;
+                }
+
                // gather target features per extract layer; the fused decode encodes and
                // injects them into the K/V cache at the target positions
                features_buf.resize((size_t) n_chunk * n_embd_enc);
@@ -1574,12 +1613,12 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
            }
        }

-        llama_set_embeddings_nextn(ctx_tgt, true, /*masked*/ false);
-        llama_set_embeddings_nextn(ctx_dft, true, /*masked*/ true);
-
        is_mem_shared = llama_get_ctx_other(ctx_dft) == ctx_tgt;
        chain_heads   = n_mtp_layers > 1 && !is_mem_shared;

+        llama_set_embeddings_nextn(ctx_tgt, true, /*masked*/ is_mem_shared);
+        llama_set_embeddings_nextn(ctx_dft, true, /*masked*/ true);
+
        if (chain_heads) {
            this->params.n_max = std::min(this->params.n_max, n_mtp_layers);

@@ -1733,16 +1772,28 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
            }

            const int32_t n_rows = i_batch_end[seq_id] - i_batch_beg[seq_id] + 1;
-            verify_h_rows[seq_id] = n_rows;
-            verify_h[seq_id].resize((size_t) n_rows * n_embd);
+            if (is_mem_shared && !batch_in.tokens[i_batch_end[seq_id]].output) {
+                verify_h_rows[seq_id] = 0;
+                continue;
+            }

-            for (int32_t i = 0; i < n_rows; ++i) {
-                const float * h = llama_get_embeddings_nextn_ith(ctx_tgt, i_batch_beg[seq_id] + i);
+            const bool all_rows_output = std::all_of(
+                    batch_in.tokens.begin() + i_batch_beg[seq_id],
+                    batch_in.tokens.begin() + i_batch_end[seq_id] + 1,
+                    [](const common_batch::token & token) { return token.output; });
+            const int32_t h_rows = is_mem_shared && !all_rows_output ? 1 : n_rows;
+            const int32_t h_offset = is_mem_shared && !all_rows_output ? i_batch_end[seq_id] : i_batch_beg[seq_id];
+
+            verify_h_rows[seq_id] = h_rows;
+            verify_h[seq_id].resize((size_t) h_rows * n_embd);
+
+            for (int32_t i = 0; i < h_rows; ++i) {
+                const float * h = llama_get_embeddings_nextn_ith(ctx_tgt, h_offset + i);
                std::memcpy(verify_h[seq_id].data() + (size_t) i * n_embd, h, row_bytes);
            }

            std::memcpy(pending_h[seq_id].data(),
-                    verify_h[seq_id].data() + (size_t) (n_rows - 1) * n_embd, row_bytes);
+                    verify_h[seq_id].data() + (size_t) (h_rows - 1) * n_embd, row_bytes);
        }

        return true;
@@ -1928,6 +1979,29 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
        const size_t row_bytes = (size_t) n_embd * sizeof(float);
        std::memcpy(pending_h[seq_id].data(), verify_h[seq_id].data() + (size_t) i_h * n_embd, row_bytes);
    }
+
+    bool get_state(llama_seq_id seq_id, std::vector<uint8_t> & data) const override {
+        if (seq_id < 0 || seq_id >= (llama_seq_id) n_seq) {
+            return false;
+        }
+
+        const size_t row_bytes = (size_t) n_embd * sizeof(float);
+        data.resize(row_bytes);
+        std::memcpy(data.data(), pending_h[seq_id].data(), row_bytes);
+        return true;
+    }
+
+    void set_state(llama_seq_id seq_id, const std::vector<uint8_t> & data) override {
+        if (seq_id < 0 || seq_id >= (llama_seq_id) n_seq) {
+            return;
+        }
+
+        const size_t row_bytes = (size_t) n_embd * sizeof(float);
+        if (data.size() != row_bytes) {
+            return;
+        }
+        std::memcpy(pending_h[seq_id].data(), data.data(), row_bytes);
+    }
 };

 // state of self-speculation (simple implementation, not ngram-map)
@@ -0,0 +1,9 @@
+<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
+<assembly xmlns="urn:schemas-microsoft-com:asm.v1" manifestVersion="1.0">
+  <application xmlns="urn:schemas-microsoft-com:asm.v3">
+    <windowsSettings>
+      <activeCodePage xmlns="http://schemas.microsoft.com/SMI/2019/WindowsSettings">UTF-8</activeCodePage>
+      <longPathAware xmlns="http://schemas.microsoft.com/SMI/2016/WindowsSettings">true</longPathAware>
+    </windowsSettings>
+  </application>
+</assembly>
@@ -45,7 +45,9 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "ChameleonForConditionalGeneration": "chameleon",
    "ChatGLMForConditionalGeneration": "chatglm",
    "ChatGLMModel": "chatglm",
+    "ChatterboxModel": "chatterbox",
    "ClefModel": "clef",
+    "CloverMoeModelLM": "clover",
    "CodeShellForCausalLM": "codeshell",
    "CogVLMForCausalLM": "cogvlm",
    "Cohere2MoeForCausalLM": "command_r",
@@ -176,6 +178,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "Lfm3ForCausalLM": "lfm3",
    "Lfm3OmniForConditionalGeneration": "lfm3",
    "Lfm3VlForConditionalGeneration": "lfm3",
+    "LimiteForCausalLM": "limite",
    "Llama4ForCausalLM": "llama",
    "Llama4ForConditionalGeneration": "llama",
    "LlamaBidirectionalModel": "llama",
@@ -195,6 +198,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "MambaLMHeadModel": "mamba",
    "MapleForCausalLM": "maple",
    "MellumForCausalLM": "mellum",
+    "Metis16ForCausalLM": "metis",
    "MiMoV2FlashForCausalLM": "mimo",
    "MiMoV2ForCausalLM": "mimo",
    "MiniCPM3ForCausalLM": "minicpm",
@@ -212,6 +216,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "Ministral3Model": "mistral3",
    "Mistral3ForConditionalGeneration": "mistral3",
    "Mistral3Model": "mistral3",
+    "MistralLarge4ForConditionalGeneration": "mistral4",
    "VoxtralRealtimeForConditionalGeneration": "mistral3",
    "MistralForCausalLM": "llama",
    "MixtralForCausalLM": "llama",
@@ -220,6 +225,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "ModernBertForSequenceClassification": "bert",
    "ModernBertModel": "bert",
    "NanbeigeForCausalLM": "nanbeige",
+    "Nemotron3_5TranscribeForConditionalGeneration": "nemotron",
    "NemotronForCausalLM": "nemotron",
    "NemotronHForCausalLM": "nemotron",
    "NemotronHPuzzleForCausalLM": "nemotron",
@@ -325,6 +331,7 @@ TEXT_MODEL_MAP: dict[str, str] = {

 MMPROJ_MODEL_MAP: dict[str, str] = {
    "AudioFlamingo3ForConditionalGeneration": "ultravox",
+    "ChatterboxModel": "chatterbox",
    "ClefModel": "clef",
    "CogVLMForCausalLM": "cogvlm",
    "Cohere2VisionForConditionalGeneration": "command_r",
@@ -377,6 +384,7 @@ MMPROJ_MODEL_MAP: dict[str, str] = {
    "MiniCPMOcrForConditionalGeneration": "minicpm",
    "Mistral3ForConditionalGeneration": "llava",
    "Mistral3Model": "llava",
+    "Nemotron3_5TranscribeForConditionalGeneration": "nemotron",
    "NemotronH_Omni_Reasoning_V3": "nemotron",
    "NemotronH_Nano_VL_V2": "nemotron",
    "MuseGlimmerForConditionalGeneration": "muse_glimmer",
@@ -16,6 +16,7 @@ from pathlib import Path
 from hashlib import sha256
 from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Literal, Sequence, TypeVar, cast
 from itertools import chain
+from math import prod
 from transformers import AutoConfig

 import numpy as np
@@ -155,6 +156,7 @@ class ModelBase:
        self.fuse_gate_up_exps = fuse_gate_up_exps
        self._gate_exp_buffer: dict[int, Tensor] = {}
        self._up_exp_buffer: dict[int, Tensor] = {}
+        self._unfusable_gate_up_layers: set[int] = set()
        self.fuse_qkv = fuse_qkv
        self._q_buffer: dict[int, Tensor] = {}
        self._k_buffer: dict[int, Tensor] = {}
@@ -175,7 +177,9 @@ class ModelBase:
        self._nvfp4_layer_algo: dict[str, str | None] = {} # per-layer quant_algo, keyed by HF module path
        self._prec_a4: dict[str, bool] = {} # gguf tensor name -> can use 4-bit (A4) activations
        self._fp8_as_q8 = fp8_as_q8
+        self._preserve_fp8 = ftype == gguf.LlamaFileType.GUESSED and not fp8_as_q8
        self._fp8_dequantized: set[str] = set()
+        self._fp8_e4m3_preserved: set[str] = set()

        # Apply heuristics to figure out typical tensor encoding based on first tensor's dtype
        # NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie.
@@ -221,8 +225,6 @@ class ModelBase:
        tensors: dict[str, Callable[[], Tensor]] = {}

        if remote_hf_model_id is not None:
-            is_safetensors = True
-
            logger.info(f"Using remote model with HuggingFace id: {remote_hf_model_id}")
            remote_tensors = gguf.utility.SafetensorRemote.get_list_tensors_hf_model(remote_hf_model_id)
            for name, remote_tensor in remote_tensors.items():
@@ -233,9 +235,13 @@ class ModelBase:

            return tensors

-        prefix = "model" if not self.is_mistral_format else "consolidated"
+        return self.index_local_tensors("model" if not self.is_mistral_format else "consolidated")
+
+    # index the local "<prefix>*.safetensors" files (or pytorch_model*.bin), through filter_tensors
+    def index_local_tensors(self, prefix: str) -> dict[str, Callable[[], Tensor]]:
+        tensors: dict[str, Callable[[], Tensor]] = {}
        part_names: list[str] = ModelBase.get_model_part_names(self.dir_model, prefix, ".safetensors")
-        is_safetensors: bool = len(part_names) > 0 or (not self.is_mistral_format and (self.dir_model / "model.safetensors.index.json").is_file())
+        is_safetensors: bool = len(part_names) > 0 or (not self.is_mistral_format and (self.dir_model / f"{prefix}.safetensors.index.json").is_file())
        if not is_safetensors:
            part_names = ModelBase.get_model_part_names(self.dir_model, "pytorch_model", ".bin")

@@ -243,7 +249,7 @@ class ModelBase:
        tensor_names_from_parts: set[str] = set()

        if not self.is_mistral_format:
-            index_name = "model.safetensors" if is_safetensors else "pytorch_model.bin"
+            index_name = f"{prefix}.safetensors" if is_safetensors else "pytorch_model.bin"
            index_name += ".index.json"
            index_file = self.dir_model / index_name

@@ -326,7 +332,7 @@ class ModelBase:

    def dequant_model(self):
        # If all quantized tensors were already handled (e.g. pure NVFP4), skip
-        if self._is_nvfp4 and not any(k.endswith((".weight_scale", ".weight_scale_inv")) for k in self.model_tensors):
+        if self._is_nvfp4 and not any(k.endswith((".weight_scale", ".weight_scale_inv", ".input_scale", ".activation_scale", "_activation_scale", ".k_scale", ".v_scale")) for k in self.model_tensors):
            return

        tensors_to_remove: list[str] = []
@@ -350,6 +356,12 @@ class ModelBase:
            def dequant_simple(weight: Tensor, scale: Tensor, block_size: Sequence[int] | None = None) -> Tensor:
                scale = scale.float()

+                # ModelOpt block scales are pre-shaped for broadcasting, e.g. [out/128, 1, in/128, 1]
+                if block_size is None and scale.ndim == 2 * weight.ndim and all(n == 1 for n in scale.shape[1::2]):
+                    assert all(w % n == 0 for w, n in zip(weight.shape, scale.shape[0::2]))
+                    block_size = [w // n for w, n in zip(weight.shape, scale.shape[0::2])]
+                    scale = scale.reshape(tuple(scale.shape[0::2]))
+
                if block_size is not None:
                    dim_offset = scale.ndim - len(block_size)
                    for i, size in enumerate(block_size):
@@ -440,7 +452,7 @@ class ModelBase:

                return (unpacked * scale.unsqueeze(-1).float()).reshape(shape)

-            def dequant_fp8() -> None:
+            def dequant_fp8(fp8_configs: list[dict[str, Any]] | None = None) -> None:
                for name in self.model_tensors.keys():
                    if name.endswith(".weight_scale"):
                        weight_name = name.removesuffix("_scale")
@@ -449,14 +461,29 @@ class ModelBase:
                            continue
                        w = self.model_tensors[weight_name]
                        s = self.model_tensors[name]
+                        block_size = None
                        is_fp8_weight = False
-                        if self._fp8_as_q8:
-                            is_fp8_weight = w().dtype in (torch.float8_e4m3fn, torch.float8_e5m2)
-                        self.model_tensors[weight_name] = lambda w=w, s=s: dequant_simple(w(), s(), None)
+                        if self._fp8_as_q8 or fp8_configs is not None:
+                            weight = w()
+                            is_fp8_weight = weight.dtype in (torch.float8_e4m3fn, torch.float8_e5m2)
+                        if fp8_configs is not None:
+                            if not is_fp8_weight:
+                                raise ValueError(f"Expected remaining weight {weight_name!r} to be FP8, got {weight.dtype}")
+                            scale = s()
+                            is_channel_scale = scale.ndim >= 1 and scale.ndim <= weight.ndim and scale.shape[0] == weight.shape[0] and all(size == 1 for size in scale.shape[1:])
+                            if prod(scale.shape) != 1 and not is_channel_scale:
+                                block_configs = [c for c in fp8_configs if c.get("strategy") == "block"]
+                                if not block_configs or any(not c.get("block_structure") or c.get("group_size") is not None for c in block_configs):
+                                    raise ValueError(f"FP8 weight {weight_name!r} with scale shape {list(scale.shape)} requires a block layout")
+                                block_sizes = {tuple(c["block_structure"]) for c in block_configs}
+                                if len(block_sizes) != 1:
+                                    raise ValueError(f"FP8 weight {weight_name!r} has ambiguous block layouts")
+                                block_size = next(iter(block_sizes))
+                        self.model_tensors[weight_name] = lambda w=w, s=s, bs=block_size: dequant_simple(w(), s(), bs)
                        tensors_to_remove.append(name)
-                        if is_fp8_weight:
+                        if self._fp8_as_q8 and is_fp8_weight:
                            self._fp8_dequantized.add(weight_name)
-                    if name.endswith((".input_scale", ".k_scale", ".v_scale")):
+                    if name.endswith((".input_scale", ".activation_scale", "_activation_scale", ".k_scale", ".v_scale")):
                        tensors_to_remove.append(name)

            if quant_method == "bitnet":
@@ -521,29 +548,37 @@ class ModelBase:
                nvfp4_compressed_tensors = self._is_nvfp4_compressed_tensors(quant_method, quant_format, groups)

                if nvfp4_compressed_tensors:
-                    dequant_fp8()
+                    fp8_configs = [
+                        g["weights"] for g in groups.values()
+                        if isinstance(g, dict) and g.get("format") == "float-quantized"
+                        and g.get("weights", {}).get("type") == "float"
+                        and g.get("weights", {}).get("num_bits") == 8
+                    ]
+                    dequant_fp8(fp8_configs)
                elif len(groups) > 1:
                    raise NotImplementedError("Can't handle multiple config groups for compressed-tensors yet")
                elif quant_format == "float-quantized" or quant_format == "int-quantized" or quant_format == "naive-quantized":
                    weight_config = tuple(groups.values())[0]["weights"]
                    block_size = weight_config.get("block_structure", None)
                    strategy = weight_config.get("strategy")
-                    assert strategy == "channel" or strategy == "block"
-                    assert weight_config.get("group_size") is None  # didn't find a model using this yet
                    is_fp8 = (
                        quant_format == "float-quantized"
                        and weight_config.get("type") == "float"
                        and weight_config.get("num_bits") == 8
                    )
+                    assert strategy in ("channel", "block") or (is_fp8 and strategy == "tensor")
+                    assert weight_config.get("group_size") is None  # didn't find a model using this yet
                    for name in self.model_tensors.keys():
                        if name.endswith(".weight_scale"):
                            weight_name = name.removesuffix("_scale")
                            w = self.model_tensors[weight_name]
                            s = self.model_tensors[name]
-                            self.model_tensors[weight_name] = lambda w=w, s=s: dequant_simple(w(), s(), block_size)
+                            self.model_tensors[weight_name] = lambda w=w, s=s, bs=block_size: dequant_simple(w(), s(), bs)
                            tensors_to_remove.append(name)
                            if self._fp8_as_q8 and is_fp8:
                                self._fp8_dequantized.add(weight_name)
+                        if is_fp8 and name.endswith((".input_scale", ".activation_scale", "_activation_scale", ".k_scale", ".v_scale")):
+                            tensors_to_remove.append(name)
                elif quant_format == "pack-quantized":
                    weight_config = tuple(groups.values())[0]["weights"]
                    assert weight_config.get("strategy") == "group"
@@ -581,6 +616,148 @@ class ModelBase:
        for name, value in new_tensors.items():
            self.model_tensors[name] = value

+    def _map_fp8_weight_names(self, name: str) -> tuple[str, ...]:
+        new_name = self.tensor_map.get_name(name, try_suffixes=(".weight",))
+        return (new_name,) if new_name is not None else ()
+
+    def _prepare_fp8_e4m3_tensors(self):
+        if not self._preserve_fp8:
+            return
+
+        scale_tensors: dict[str, list[tuple[int, float]] | np.ndarray] = {}
+        input_scale_tensors: dict[str, list[tuple[int, float]] | np.ndarray] = {}
+        consumed: list[str] = []
+        candidates: dict[str, tuple[str, Tensor, tuple[str, ...], torch.Size]] = {}
+        weight_dtypes: dict[str, torch.dtype] = {}
+        scaled_weights: set[str] = set()
+        expert_pattern = re.compile(r"(\.[^.]*experts)\.(\d+)\.")
+        scale_types = {
+            gguf.MODEL_TENSOR.ATTN_Q, gguf.MODEL_TENSOR.ATTN_K, gguf.MODEL_TENSOR.ATTN_V,
+            gguf.MODEL_TENSOR.ATTN_OUT, gguf.MODEL_TENSOR.ATTN_QKV, gguf.MODEL_TENSOR.ATTN_GATE,
+            gguf.MODEL_TENSOR.FFN_GATE, gguf.MODEL_TENSOR.FFN_UP, gguf.MODEL_TENSOR.FFN_DOWN,
+            gguf.MODEL_TENSOR.FFN_GATE_SHEXP, gguf.MODEL_TENSOR.FFN_UP_SHEXP, gguf.MODEL_TENSOR.FFN_DOWN_SHEXP,
+            gguf.MODEL_TENSOR.FFN_GATE_EXP, gguf.MODEL_TENSOR.FFN_UP_EXP, gguf.MODEL_TENSOR.FFN_DOWN_EXP,
+            gguf.MODEL_TENSOR.SSM_IN, gguf.MODEL_TENSOR.SSM_OUT, gguf.MODEL_TENSOR.SSM_ALPHA, gguf.MODEL_TENSOR.SSM_BETA,
+            gguf.MODEL_TENSOR.NEXTN_EH_PROJ, gguf.MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD, gguf.MODEL_TENSOR.OUTPUT,
+        }
+
+        for scale_name in list(self.model_tensors.keys()):
+            weight_name = None
+            if scale_name.endswith(".weight_scale"):
+                weight_name = scale_name.removesuffix("_scale")
+            elif scale_name.endswith("_scale_inv"):
+                weight_name = scale_name.removesuffix("_scale_inv")
+            elif scale_name.endswith(".qscale_weight"):
+                weight_name = scale_name.removesuffix("qscale_weight") + "weight"
+
+            if weight_name is None or weight_name not in self.model_tensors:
+                continue
+
+            scaled_weights.add(weight_name)
+            weight = self.model_tensors[weight_name]()
+            weight_dtypes[weight_name] = weight.dtype
+            if weight.dtype != torch.float8_e4m3fn or weight.ndim < 2:
+                continue
+
+            scale = self.model_tensors[scale_name]()
+            if prod(scale.shape) != 1:
+                continue
+
+            merged_name = expert_pattern.sub(r"\1.", weight_name)
+            new_names = self._map_fp8_weight_names(merged_name)
+            if not new_names:
+                continue
+            if any((mapped := self.tensor_map.get_type_and_name(new_name, try_suffixes=(".weight",))) is None or mapped[0] not in scale_types for new_name in new_names):
+                continue
+            bid = next((int(part) for part in new_names[0].split(".") if part.isdecimal()), None)
+            self._fp8_e4m3_preserved.add(weight_name)
+            if any(new_name.endswith("_norm.weight") or self.tensor_force_quant(weight_name, new_name, bid, weight.ndim) != gguf.GGMLQuantizationType.F8_E4M3 for new_name in new_names):
+                self._fp8_e4m3_preserved.discard(weight_name)
+                continue
+            candidates[weight_name] = (scale_name, scale, new_names, weight.shape)
+
+        expert_groups: dict[str, list[str]] = {}
+        for name in self.model_tensors:
+            if (match := expert_pattern.search(name)) and name.endswith(".weight"):
+                expert_groups.setdefault(name[:match.end(1)], []).append(name)
+        for names in expert_groups.values():
+            for name in names:
+                if name not in weight_dtypes:
+                    weight_dtypes[name] = self.model_tensors[name]().dtype
+            dtypes = {weight_dtypes[name] for name in names}
+            if torch.float8_e4m3fn not in dtypes:
+                continue
+            if dtypes == {torch.float8_e4m3fn} and not any(name in scaled_weights for name in names):
+                continue
+            # The converter merges expert projections together.
+            if not all(name in candidates for name in names):
+                for name in names:
+                    candidates.pop(name, None)
+                    self._fp8_e4m3_preserved.discard(name)
+                    if name not in scaled_weights:
+                        gen = self.model_tensors[name]
+                        self.model_tensors[name] = lambda gen=gen: gen().float()
+
+        for weight_name, (scale_name, scale, new_names, shape) in candidates.items():
+            scale = LazyTorchTensor.to_eager(scale).float().flatten()
+
+            weight_prefix = weight_name.removesuffix(".weight")
+            # Transformers fine-grained FP8 uses activation_scale while ModelOpt uses input_scale.
+            # Ref: https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/mistral3.py#L357-L361
+            input_scale_name = next((name for name in (
+                weight_prefix + ".input_scale",
+                weight_prefix + ".activation_scale",
+                weight_prefix + "_activation_scale",
+                weight_prefix + ".qscale_act",
+            ) if name in self.model_tensors), None)
+            input_scale = None
+            if input_scale_name is not None:
+                input_scale = self.model_tensors[input_scale_name]()
+                if prod(input_scale.shape) != 1:
+                    raise ValueError(f"FP8 input scale {input_scale_name!r} must be a scalar")
+                input_scale = LazyTorchTensor.to_eager(input_scale).float().flatten()
+                consumed.append(input_scale_name)
+
+            consumed.append(scale_name)
+
+            expert_match = expert_pattern.search(weight_name)
+            if expert_match:
+                expert_id = int(expert_match.group(2))
+                for new_name in new_names:
+                    target_name = new_name.replace(".weight", ".scale")
+                    entries = scale_tensors.setdefault(target_name, [])
+                    assert isinstance(entries, list)
+                    cast(list[tuple[int, float]], entries).append((expert_id, float(scale[0])))
+                    if input_scale is not None:
+                        target_name = new_name.replace(".weight", ".input_scale")
+                        entries = input_scale_tensors.setdefault(target_name, [])
+                        assert isinstance(entries, list)
+                        cast(list[tuple[int, float]], entries).append((expert_id, float(input_scale[0])))
+            else:
+                for new_name in new_names:
+                    mapped = self.tensor_map.get_type_and_name(new_name, try_suffixes=(".weight",)) if len(shape) == 3 else None
+                    is_packed_expert = mapped is not None and mapped[0] in (
+                        gguf.MODEL_TENSOR.FFN_GATE_EXP, gguf.MODEL_TENSOR.FFN_UP_EXP, gguf.MODEL_TENSOR.FFN_DOWN_EXP,
+                    )
+                    # Packed expert weights need one scale value per expert.
+                    n_scales = shape[0] if is_packed_expert else 1
+                    scale_tensors[new_name.replace(".weight", ".scale")] = np.repeat(scale.numpy(), n_scales)
+                    if input_scale is not None:
+                        input_scale_tensors[new_name.replace(".weight", ".input_scale")] = np.repeat(input_scale.numpy(), n_scales)
+
+        for name in consumed:
+            self.model_tensors.pop(name, None)
+
+        for name, values in chain(scale_tensors.items(), input_scale_tensors.items()):
+            if isinstance(values, list):
+                values.sort(key=lambda item: item[0])
+                scale = np.array([item[1] for item in values], dtype=np.float32)
+            else:
+                scale = values.astype(np.float32)
+            if not np.allclose(scale, 1.0, atol=1e-6):
+                logger.info(f"  + {name} (FP8 scale, shape [{scale.size}])")
+                self.gguf_writer.add_tensor(name, scale)
+
    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item
@@ -625,6 +802,26 @@ class ModelBase:
            raise ValueError(f"Can not map tensor {name!r}")
        return new_name

+    def prepare_gate_up_fusion(self) -> None:
+        self._unfusable_gate_up_layers.clear()
+        if not self.fuse_gate_up_exps or self._fp8_as_q8:
+            return
+
+        for name, gen in self.model_tensors.items():
+            merged_name = re.sub(r"(\.experts)\.\d+\.", r"\1.", name)
+            mapped = self.tensor_map.get_type_and_name(merged_name, try_suffixes=(".weight",))
+            if mapped is None:
+                continue
+            tensor_type, new_name = mapped
+            if tensor_type not in (gguf.MODEL_TENSOR.FFN_GATE_EXP, gguf.MODEL_TENSOR.FFN_UP_EXP, gguf.MODEL_TENSOR.FFN_GATE_UP_EXP) or not new_name.endswith(".weight"):
+                continue
+            if gen().dtype == torch.float8_e4m3fn:
+                bid = next(int(part) for part in new_name.split(".") if part.isdecimal())
+                self._unfusable_gate_up_layers.add(bid)
+
+        for bid in sorted(self._unfusable_gate_up_layers):
+            logger.warning(f"Skipping --fuse-gate-up-exps for layer {bid}: fusion of preserved FP8 expert projections is not supported")
+
    def prepare_qkv_fusion(self) -> None:
        self._fusable_qkv_weight_layers.clear()
        self._fusable_qkv_bias_layers.clear()
@@ -638,6 +835,7 @@ class ModelBase:
        }
        weights: dict[int, set[gguf.MODEL_TENSOR]] = {}
        biases: dict[int, set[gguf.MODEL_TENSOR]] = {}
+        fp8_layers: set[int] = set()

        for name in self.model_tensors:
            mapped = self.tensor_map.get_type_and_name(name, try_suffixes=(".weight", ".bias"))
@@ -652,12 +850,19 @@ class ModelBase:
                continue
            if new_name.endswith(".weight"):
                weights.setdefault(bid, set()).add(tensor_type)
+                if not self._fp8_as_q8 and self.model_tensors[name]().dtype == torch.float8_e4m3fn:
+                    fp8_layers.add(bid)
            elif new_name.endswith(".bias"):
                biases.setdefault(bid, set()).add(tensor_type)

        for bid, weight_types in weights.items():
            bias_types = biases.get(bid, set())
            if weight_types == qkv_types and (not bias_types or bias_types == qkv_types):
+                # NVFP4 weights use separate reordering and repacking hooks and are exported before this fusion pass.
+                # Separate FP8 scales cannot be represented by one fused QKV scale.
+                if bid in fp8_layers:
+                    logger.warning(f"Skipping --fuse-qkv for layer {bid}: fusion of preserved FP8 projections is not supported")
+                    continue
                self._fusable_qkv_weight_layers.add(bid)
                if bias_types:
                    self._fusable_qkv_bias_layers.add(bid)
@@ -846,7 +1051,8 @@ class ModelBase:
        new_name = self.map_tensor_name(name)

        # Handle gate/up expert tensor fusion if enabled
-        if self.fuse_gate_up_exps and bid is not None:
+        # Preserved FP8 gate/up scales must stay with their separate projections.
+        if self.fuse_gate_up_exps and bid is not None and bid not in self._unfusable_gate_up_layers:
            if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_GATE_EXP, bid):
                self._gate_exp_buffer[bid] = data_torch
            elif self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_UP_EXP, bid):
@@ -903,8 +1109,46 @@ class ModelBase:

        return [(new_name, data_torch)]

+    def _tensor_requires_f32(self, new_name: str, bid: int | None, n_dims: int) -> bool:
+        # Conditions should closely match those in llama_model_quantize_internal in llama.cpp.
+        return n_dims <= 1 or new_name.endswith("_norm.weight") or (
+            any(
+                self.match_model_tensor_name(new_name, key, bid)
+                for key in (
+                    gguf.MODEL_TENSOR.FFN_GATE_INP,
+                    gguf.MODEL_TENSOR.FFN_GATE_INP_SHEXP,
+                    gguf.MODEL_TENSOR.POS_EMBD,
+                    gguf.MODEL_TENSOR.TOKEN_TYPES,
+                    gguf.MODEL_TENSOR.SSM_CONV1D,
+                    gguf.MODEL_TENSOR.SHORTCONV_CONV,
+                    gguf.MODEL_TENSOR.TIME_MIX_FIRST,
+                    gguf.MODEL_TENSOR.TIME_MIX_W1,
+                    gguf.MODEL_TENSOR.TIME_MIX_W2,
+                    gguf.MODEL_TENSOR.TIME_MIX_DECAY_W1,
+                    gguf.MODEL_TENSOR.TIME_MIX_DECAY_W2,
+                    gguf.MODEL_TENSOR.TIME_MIX_LERP_FUSED,
+                    gguf.MODEL_TENSOR.POSNET_NORM1,
+                    gguf.MODEL_TENSOR.POSNET_NORM2,
+                    gguf.MODEL_TENSOR.V_ENC_EMBD_POS,
+                    gguf.MODEL_TENSOR.A_ENC_EMBD_POS,
+                    gguf.MODEL_TENSOR.ALTUP_CORRECT_COEF,
+                    gguf.MODEL_TENSOR.ALTUP_PREDICT_COEF,
+                    # Kimi KDA conv weights should be F32
+                    gguf.MODEL_TENSOR.SSM_CONV1D_Q,
+                    gguf.MODEL_TENSOR.SSM_CONV1D_K,
+                    gguf.MODEL_TENSOR.SSM_CONV1D_V,
+                    # DSA indexer weights should be F32
+                    gguf.MODEL_TENSOR.INDEXER_PROJ,
+                )
+            )
+            or new_name[-7:] not in (".weight", ".lora_a", ".lora_b")
+        )
+
    def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
-        del new_name, bid  # unused
+        if name in self._fp8_e4m3_preserved and n_dims >= 2:
+            if self._tensor_requires_f32(new_name, bid, n_dims):
+                return gguf.GGMLQuantizationType.F32
+            return gguf.GGMLQuantizationType.F8_E4M3
        # Force FP8-original tensors to Q8_0 when requested; Q8_0 is faster than F16/BF16.
        if self._fp8_as_q8 and name in self._fp8_dequantized and n_dims >= 2:
            return gguf.GGMLQuantizationType.Q8_0
@@ -1035,7 +1279,8 @@ class ModelBase:
        expert_scales: dict[tuple[int, str], list[tuple[int, float]]] = {}
        expert_input_scales: dict[tuple[int, str], list[tuple[int, float]]] = {}
        expert_shapes: dict[tuple[int, str], list[int]] = {}
-        n_experts = self.find_hparam(["num_local_experts", "num_experts"], optional=True) or 0
+        expert_names: dict[tuple[int, str], str] = {}
+        n_experts = self.find_hparam(["num_local_experts", "num_experts", "n_routed_experts"], optional=True) or 0
        consumed: list[str] = []

        for name in self.model_tensors.keys():
@@ -1046,14 +1291,17 @@ class ModelBase:
            input_scale_name = name.replace(".weight", ".input_scale")
            if scale_name not in self.model_tensors:
                continue
-            # Force eager materialization of lazy tensors
-            weight = LazyTorchTensor.to_eager(self.model_tensors[name]())
-            scale = LazyTorchTensor.to_eager(self.model_tensors[scale_name]())
+            weight = self.model_tensors[name]()
+            scale = self.model_tensors[scale_name]()

-            # Skip non-NVFP4 tensors(e.g. 1D scale, or float8 weight)
-            if scale.ndim < 2 or weight.dtype in (torch.float8_e4m3fn, torch.float8_e5m2):
+            # Leave FP8 weights and their scales for _prepare_fp8_e4m3_tensors.
+            if weight.dtype in (torch.float8_e4m3fn, torch.float8_e5m2) or scale.ndim < 2:
                continue
+            if weight.ndim != 2 or scale.shape[0] != weight.shape[0] or scale.shape[1] * 8 != weight.shape[1]:
+                raise ValueError(f"NVFP4 weight {name!r} has incompatible shapes: weight {list(weight.shape)}, scale {list(scale.shape)}")

+            weight = LazyTorchTensor.to_eager(weight)
+            scale = LazyTorchTensor.to_eager(scale)
            scale2 = LazyTorchTensor.to_eager(self.model_tensors.get(scale2_name, lambda: torch.tensor(1.0))())
            input_scale = LazyTorchTensor.to_eager(self.model_tensors.get(input_scale_name, lambda: torch.tensor(1.0))())

@@ -1080,6 +1328,7 @@ class ModelBase:
                    expert_scales[key] = []
                    expert_input_scales[key] = []
                    expert_shapes[key] = shape
+                    expert_names[key] = name
                expert_blocks[key].append((expert_id, raw.copy()))
                # Collect per-expert scale2 (scalar per expert)
                expert_scales[key].append((expert_id, float(scale2.float().sum())))
@@ -1088,13 +1337,13 @@ class ModelBase:

                # Flush when all experts for this (layer, proj) are collected
                if n_experts > 0 and len(expert_blocks[key]) >= n_experts:
-                    self._flush_nvfp4_experts(key, expert_blocks, expert_scales, expert_input_scales, expert_shapes, bid, proj_type)
+                    self._flush_nvfp4_experts(key, expert_blocks, expert_scales, expert_input_scales, expert_shapes, expert_names, bid, proj_type)
            else:
                self._repack_nvfp4(name, weight, scale, scale2, input_scale)

        # Flush any remaining experts (fallback if n_experts was unknown)
        for bid, proj_type in list(expert_blocks.keys()):
-            self._flush_nvfp4_experts((bid, proj_type), expert_blocks, expert_scales, expert_input_scales, expert_shapes, bid, proj_type)
+            self._flush_nvfp4_experts((bid, proj_type), expert_blocks, expert_scales, expert_input_scales, expert_shapes, expert_names, bid, proj_type)

        # Remove consumed tensors so get_tensors/modify_tensors won't see them
        for name in consumed:
@@ -1105,7 +1354,7 @@ class ModelBase:
            if name.endswith((".k_scale", ".v_scale")):
                del self.model_tensors[name]

-    def _flush_nvfp4_experts(self, key, expert_blocks, expert_scales, expert_input_scales, expert_shapes, bid, proj_type):
+    def _flush_nvfp4_experts(self, key, expert_blocks, expert_scales, expert_input_scales, expert_shapes, expert_names, bid, proj_type):
        experts = expert_blocks.pop(key)
        scales = expert_scales.pop(key)
        input_scales = expert_input_scales.pop(key)
@@ -1117,7 +1366,7 @@ class ModelBase:
        new_name = self.map_tensor_name(merged_name)
        logger.info(f"Repacked {new_name} with shape [{len(experts)}, {shape[0]}, {shape[1]}] and quantization NVFP4")
        self.gguf_writer.add_tensor(new_name, merged, raw_dtype=gguf.GGMLQuantizationType.NVFP4)
-        self._tag_prec_a4(merged_name, new_name)
+        self._tag_prec_a4(expert_names.pop(key), new_name)

        scales.sort(key=lambda x: x[0])
        self._write_scales_tensor(new_name.replace(".weight", ".scale"), [s[1] for s in scales])
@@ -1209,8 +1458,10 @@ class ModelBase:
                            self.model_tensors[input_scale_name] = inverse_scale(self.model_tensors.pop(name))
            self._generate_nvfp4_tensors()

+        self._prepare_fp8_e4m3_tensors()
        self.dequant_model()

+        self.prepare_gate_up_fusion()
        self.prepare_qkv_fusion()

        # Handle empty tensor_map for models with block_count=0 (like MobileNetV5)
@@ -1224,10 +1475,21 @@ class ModelBase:
            if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
                continue

+            # FP8 transforms must only reorder values; norms and vectors use numeric F32 transforms.
+            preserve_fp8_e4m3 = self._preserve_fp8 and data_torch.dtype == torch.float8_e4m3fn and data_torch.ndim >= 2 and not name.endswith(("norm.weight", "norm_f.weight"))
+            if preserve_fp8_e4m3:
+                for new_name in self._map_fp8_weight_names(name):
+                    mapped_bid = next((int(part) for part in new_name.split(".") if part.isdecimal()), None)
+                    if self._tensor_requires_f32(new_name, mapped_bid, data_torch.ndim):
+                        preserve_fp8_e4m3 = False
+                        break
+                if preserve_fp8_e4m3:
+                    self._fp8_e4m3_preserved.add(name)
+
            old_dtype = data_torch.dtype

            # convert any unsupported data types to float32
-            if data_torch.dtype not in (torch.float16, torch.float32):
+            if data_torch.dtype not in (torch.float16, torch.float32) and name not in self._fp8_e4m3_preserved:
                data_torch = data_torch.to(torch.float32)

            # use the first number-like part of the tensor name as the block id
@@ -1238,51 +1500,14 @@ class ModelBase:
                    break

            for new_name, data_torch in (self.modify_tensors(data_torch, name, bid)):
-                # TODO: why do we squeeze here?
-                # data = data_torch.squeeze().numpy()
-                data = data_torch.numpy()
-
-                n_dims = len(data.shape)
+                n_dims = len(data_torch.shape)
                data_qtype: gguf.GGMLQuantizationType | bool = self.tensor_force_quant(name, new_name, bid, n_dims)

                # Most of the codebase that takes in 1D tensors or norms only handles F32 tensors
                if n_dims <= 1 or new_name.endswith("_norm.weight"):
                    data_qtype = gguf.GGMLQuantizationType.F32

-                # Conditions should closely match those in llama_model_quantize_internal in llama.cpp
-                # Some tensor types are always in float32
-                if data_qtype is False and (
-                    any(
-                        self.match_model_tensor_name(new_name, key, bid)
-                        for key in (
-                            gguf.MODEL_TENSOR.FFN_GATE_INP,
-                            gguf.MODEL_TENSOR.FFN_GATE_INP_SHEXP,
-                            gguf.MODEL_TENSOR.POS_EMBD,
-                            gguf.MODEL_TENSOR.TOKEN_TYPES,
-                            gguf.MODEL_TENSOR.SSM_CONV1D,
-                            gguf.MODEL_TENSOR.SHORTCONV_CONV,
-                            gguf.MODEL_TENSOR.TIME_MIX_FIRST,
-                            gguf.MODEL_TENSOR.TIME_MIX_W1,
-                            gguf.MODEL_TENSOR.TIME_MIX_W2,
-                            gguf.MODEL_TENSOR.TIME_MIX_DECAY_W1,
-                            gguf.MODEL_TENSOR.TIME_MIX_DECAY_W2,
-                            gguf.MODEL_TENSOR.TIME_MIX_LERP_FUSED,
-                            gguf.MODEL_TENSOR.POSNET_NORM1,
-                            gguf.MODEL_TENSOR.POSNET_NORM2,
-                            gguf.MODEL_TENSOR.V_ENC_EMBD_POS,
-                            gguf.MODEL_TENSOR.A_ENC_EMBD_POS,
-                            gguf.MODEL_TENSOR.ALTUP_CORRECT_COEF,
-                            gguf.MODEL_TENSOR.ALTUP_PREDICT_COEF,
-                            # Kimi KDA conv weights should be F32
-                            gguf.MODEL_TENSOR.SSM_CONV1D_Q,
-                            gguf.MODEL_TENSOR.SSM_CONV1D_K,
-                            gguf.MODEL_TENSOR.SSM_CONV1D_V,
-                            # DSA indexer weights should be F32
-                            gguf.MODEL_TENSOR.INDEXER_PROJ,
-                        )
-                    )
-                    or new_name[-7:] not in (".weight", ".lora_a", ".lora_b")
-                ):
+                if data_qtype is False and self._tensor_requires_f32(new_name, bid, n_dims):
                    data_qtype = gguf.GGMLQuantizationType.F32

                if data_qtype is False and any(
@@ -1320,16 +1545,22 @@ class ModelBase:
                    else:
                        raise ValueError(f"Unknown file type: {self.ftype.name}")

+                if data_torch.dtype == torch.float8_e4m3fn:
+                    data = data_torch.view(torch.uint8).numpy() if data_qtype == gguf.GGMLQuantizationType.F8_E4M3 else data_torch.float().numpy()
+                else:
+                    data = data_torch.numpy()
+
                # a chunked tensor quantizes as one chunk at a time, while it is written
                quantize = data.quantize if isinstance(data, gguf.LazyChunkedTensor) else (
                    lambda qtype, d=data: gguf.quants.quantize(d, qtype))

-                try:
-                    data = quantize(data_qtype)
-                except gguf.QuantError as e:
-                    logger.warning("%s, %s", e, "falling back to F16")
-                    data_qtype = gguf.GGMLQuantizationType.F16
-                    data = quantize(data_qtype)
+                if not (data_qtype == gguf.GGMLQuantizationType.F8_E4M3 and data.dtype == np.uint8):
+                    try:
+                        data = quantize(data_qtype)
+                    except gguf.QuantError as e:
+                        logger.warning("%s, %s", e, "falling back to F16")
+                        data_qtype = gguf.GGMLQuantizationType.F16
+                        data = quantize(data_qtype)

                shape = gguf.quant_shape_from_byte_shape(data.shape, data_qtype) if data.dtype == np.uint8 else data.shape

@@ -1370,6 +1601,8 @@ class ModelBase:
                self.ftype = gguf.LlamaFileType.MOSTLY_NVFP4
            elif self._is_mxfp4:
                self.ftype = gguf.LlamaFileType.MOSTLY_MXFP4_MOE
+            elif self._fp8_e4m3_preserved:
+                self.ftype = gguf.LlamaFileType.MOSTLY_F8_E4M3

        # Generate parameter weight class (useful for leader boards) if not yet determined
        if self.metadata.size_label is None and total_params > 0:
@@ -1885,6 +2118,9 @@ class TextModel(ModelBase):
        if chkhsh == "0fe1cf6eda062318a1af7270f3331a85c539a01778ff948e24388e949c5282f4":
            # ref: https://huggingface.co/evilfreelancer/ruGPT3XL
            res = "gpt-2"
+        if chkhsh == "df1bc34a6366466bcb36a9a1b1cc0afdb08cfe21c9250602c930f159085390a9":
+            # ref: https://huggingface.co/Lernex/Metis-1.6-Logos
+            res = "gpt-2"
        if chkhsh == "9e454714343b69b99b71795c1d27a68c2a1d15dab111f4d353109f966af29da7":
            # ref: https://huggingface.co/LiquidAI/LFM2.5-8B-A1B
            res = "lfm2"
@@ -2672,7 +2908,13 @@ class TextModel(ModelBase):
        )

        if vocab.tokenizer_type == MistralTokenizerType.tekken:
-            self.gguf_writer.add_tokenizer_pre("tekken")
+            with open(vocab.fname_tokenizer, encoding="utf-8") as f:
+                pattern = json.load(f)["config"]["pattern"]
+            # newer tekken.json split numbers into right-aligned groups of 3 digits instead of single digits
+            if "\\p{N}{1,3}(?=(?:\\p{N}{3})*(?!\\p{N}))" in pattern:
+                self.gguf_writer.add_tokenizer_pre("tekken-num3")
+            else:
+                self.gguf_writer.add_tokenizer_pre("tekken")
            self.gguf_writer.add_token_merges(
                vocab.extract_vocab_merges_from_model()
            )
@@ -3149,7 +3391,7 @@ def get_model_architecture(hparams: dict[str, Any], model_type: ModelType) -> st
    # Kimi-K3's text_config reports "KimiLinearForCausalLM", which is the older
    # Kimi-Linear-48B architecture and cannot load K3 (no attention residuals,
    # latent MoE, situ, ...). Route on the top-level architecture instead.
-    if model_type == ModelType.TEXT and arch in ("NEOChatModel", "StepVLForConditionalGeneration", "Sarashina2VisionForCausalLM", "Exaone4_5_ForConditionalGeneration", "Step3p7ForConditionalGeneration", "KimiK3ForConditionalGeneration"):
+    if model_type == ModelType.TEXT and arch in ("NEOChatModel", "StepVLForConditionalGeneration", "Sarashina2VisionForCausalLM", "Exaone4_5_ForConditionalGeneration", "Step3p7ForConditionalGeneration", "KimiK3ForConditionalGeneration", "Nemotron3_5TranscribeForConditionalGeneration"):
        return arch

    # if "architectures" is found in the sub-config, use that instead
@@ -0,0 +1,471 @@
+# Chatterbox (ResembleAI) turbo and multilingual v3, from the raw checkpoints of the official repos
+# the checkpoints ship no config.json, the variant is detected from the talker file and the hparams come from the reference code
+# talker: text_emb and speech_emb are fused into one [text | speech] vocab, speech token i is <|speech_i|>
+# mmproj: the s3gen sidecar, the voice and conditioning encoders, the position and speech tables, and the conds.pt voice
+
+from __future__ import annotations
+
+import json
+import math
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Callable, Iterable
+
+import torch
+import torch.nn.functional as F
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, MmprojModel, LazyTorchTensor, gguf, logger
+from .llama import LlamaModel
+
+# checkpoint file prefixes, the files are "<prefix>.safetensors"
+TURBO_TALKER = "t3_turbo_v1"
+MTL_TALKER   = "t3_mtl23ls_v3"
+TURBO_S3GEN  = "s3gen_meanflow"
+MTL_S3GEN    = "s3gen_v3"
+VE           = "ve"
+
+# relative speech ids shared by both variants (start/stop_speech_token in the reference)
+SPEECH_BOS = 6561
+SPEECH_EOS = 6562
+
+# sampling defaults of the reference, samplers it does not use are disabled so the common defaults do not apply
+TURBO_TOP_K = 1000
+MTL_TOP_K   = 0
+TURBO_SAMPLING = {"min_p": 0.0,  "top_p": 0.95, "temp": 0.8, "penalty_repeat": 1.2}
+MTL_SAMPLING   = {"min_p": 0.05, "top_p": 1.0,  "temp": 0.8, "penalty_repeat": 1.2}
+# the reference repetition penalty covers the whole generated history
+PENALTY_LAST_N = -1
+
+
+def _has_file(dir_model: Path, prefix: str) -> bool:
+    return (dir_model / f"{prefix}.safetensors").is_file()
+
+
+def _is_turbo(dir_model: Path) -> bool:
+    if _has_file(dir_model, TURBO_TALKER):
+        return True
+    if _has_file(dir_model, MTL_TALKER):
+        return False
+    raise FileNotFoundError(f"no chatterbox talker checkpoint in {dir_model}")
+
+
+# talker hparams of the reference (GPT2_MEDIUM_CONFIG / LLAMA_520M_CONFIG_DICT, T3Config)
+TURBO_TEXT_CONFIG = {
+    "n_layer": 24, "n_embd": 1024, "n_inner": 4096, "n_head": 16, "n_ctx": 8196, "layer_norm_epsilon": 1e-05,
+    "vocab_size": 50276, "speech_vocab_size": 6563,
+}
+MTL_TEXT_CONFIG = {
+    "num_hidden_layers": 30, "hidden_size": 1024, "intermediate_size": 4096, "num_attention_heads": 16,
+    "num_key_value_heads": 16, "head_dim": 64, "max_position_embeddings": 131072, "rms_norm_eps": 1e-05,
+    "rope_theta": 500000.0,
+    "rope_scaling": {"factor": 8.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192},
+    "vocab_size": 2454, "speech_vocab_size": 8194,
+}
+
+
+def _is_chatterbox_checkpoint(dir_model: Path) -> bool:
+    return _has_file(dir_model, VE) and (_has_file(dir_model, TURBO_TALKER) or _has_file(dir_model, MTL_TALKER))
+
+
+@ModelBase.register_hparams_loader(_is_chatterbox_checkpoint)
+def _load_chatterbox_hparams(dir_model: Path) -> dict[str, Any]:
+    turbo = _is_turbo(dir_model)
+    logger.info(f"gguf: detected Chatterbox {'turbo' if turbo else 'multilingual v3'} checkpoint")
+    text_config = dict(TURBO_TEXT_CONFIG if turbo else MTL_TEXT_CONFIG)
+    # the mmproj tensors are named explicitly, they do not go through the block tensor map
+    return {"architectures": ["ChatterboxModel"], "text_config": text_config, "audio_config": {"num_hidden_layers": 0}}
+
+
+@ModelBase.register("ChatterboxModel")
+class ChatterboxTalkerModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.LLAMA  # multilingual; the turbo constructor switches to GPT2
+
+    def __init__(self, dir_model: Path, *args, **kwargs):
+        self.is_turbo = _is_turbo(dir_model)
+        if self.is_turbo:
+            self.model_arch = gguf.MODEL_ARCH.GPT2
+        super().__init__(dir_model, *args, **kwargs)
+        self._text_embd: Tensor | None = None
+        self._speech_embd: Tensor | None = None
+        self._text_head: Tensor | None = None
+        self._speech_head: Tensor | None = None
+
+    def index_tensors(self, remote_hf_model_id: str | None = None) -> dict[str, Callable[[], Tensor]]:
+        return self.index_local_tensors(TURBO_TALKER if self.is_turbo else MTL_TALKER)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, _ = item
+        # the reference feeds inputs_embeds, the transformer input embeddings are unused
+        if name in ("tfmr.wte.weight", "tfmr.embed_tokens.weight"):
+            return None
+        if name.startswith(("cond_enc.", "text_pos_emb.", "speech_pos_emb.")):
+            return None
+        return super().filter_tensors(item)
+
+    def set_vocab(self):
+        if self.is_turbo:
+            self._set_vocab_turbo()
+        else:
+            self._set_vocab_mtl()
+
+    def _speech_token_names(self, n_speech: int) -> list[str]:
+        return [f"<|speech_{i}|>" for i in range(n_speech)]
+
+    def _set_vocab_turbo(self):
+        # stock GPT-2 BPE from the checkpoint dir, extended with the speech tokens
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        n_text = len(tokens)
+        n_speech = self.hparams["speech_vocab_size"]
+        speech = self._speech_token_names(n_speech)
+        tokens += speech
+        toktypes += [gguf.TokenType.CONTROL] * n_speech
+
+        with open(self.dir_model / "merges.txt", "r", encoding="utf-8") as f:
+            merges = [line.rstrip("\n") for line in f if line.strip() and not line.startswith("#version")]
+
+        self._write_vocab(tokens, toktypes, merges, tokpre, n_text)
+
+    def _write_vocab(self, tokens: list[str], toktypes: list[int], merges: list[str], tokpre: str, n_text: int):
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+        self.gguf_writer.add_token_merges(merges)
+
+        self.gguf_writer.add_bos_token_id(n_text + SPEECH_BOS)
+        self.gguf_writer.add_eos_token_id(n_text + SPEECH_EOS)
+        self.gguf_writer.add_add_bos_token(False)
+        self.gguf_writer.add_add_eos_token(False)
+
+        # the reference samples the speech head only
+        self.gguf_writer.add_suppress_tokens(list(range(n_text)))
+
+    def _set_vocab_mtl(self):
+        # the reference BPE is char-level, it is re-encoded to byte-level with extra merges that rebuild each multi-byte char
+        # its lowercasing is folded into the embedding table, so the prompt needs no text preprocessing
+        with open(self.dir_model / "mtl_tokenizer.json", "r", encoding="utf-8") as f:
+            tok = json.load(f)
+
+        byte_map = gguf.vocab.bytes_to_unicode()
+
+        def enc(s: str) -> str:
+            return "".join(byte_map[b] for b in s.encode("utf-8"))
+
+        n_text = self.hparams["vocab_size"]
+        tokens: list[str] = [f"[unused_{i}]" for i in range(n_text)]
+        toktypes = [int(gguf.TokenType.UNUSED)] * n_text
+        char_merges: list[str] = []
+        for t, i in tok["model"]["vocab"].items():
+            if t == " ":
+                # the reference substitutes [SPACE] before encoding, the raw space token is never used
+                continue
+            tokens[i] = enc(t)
+            toktypes[i] = int(gguf.TokenType.NORMAL)
+            if len(t) == 1 and len(t.encode("utf-8")) > 1:
+                parts = [byte_map[b] for b in t.encode("utf-8")]
+                for k in range(1, len(parts)):
+                    char_merges.append("".join(parts[:k]) + " " + parts[k])
+        for entry in tok.get("added_tokens", []):
+            tokens[entry["id"]] = entry["content"]
+            toktypes[entry["id"]] = int(gguf.TokenType.CONTROL)
+
+        # raw spaces tokenize to [SPACE] directly
+        space_id = tok["model"]["vocab"]["[SPACE]"]
+        tokens[space_id] = enc(" ")
+        toktypes[space_id] = int(gguf.TokenType.NORMAL)
+
+        n_speech = self.hparams["speech_vocab_size"]
+        tokens += self._speech_token_names(n_speech)
+        toktypes += [int(gguf.TokenType.CONTROL)] * n_speech
+
+        # chars are atomic in the reference, they must form before any of its merges
+        merges = char_merges
+        for m in tok["model"].get("merges", []):
+            a, b = m if isinstance(m, list) else m.split(" ")
+            merges.append(enc(a) + " " + enc(b))
+
+        self._write_vocab(tokens, toktypes, merges, "default", n_text)
+
+    def _mtl_case_fold_pairs(self) -> list[tuple[int, int]]:
+        # (upper_id, lower_id) of the single-char vocab entries, for every script in the vocab
+        with open(self.dir_model / "mtl_tokenizer.json", "r", encoding="utf-8") as f:
+            vocab = json.load(f)["model"]["vocab"]
+        pairs: list[tuple[int, int]] = []
+        for t, i in vocab.items():
+            if len(t) != 1:
+                continue
+            low = t.lower()
+            if low != t and len(low) == 1 and low in vocab:
+                pairs.append((i, vocab[low]))
+        return pairs
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        sampling = TURBO_SAMPLING if self.is_turbo else MTL_SAMPLING
+        self.gguf_writer.add_sampling_top_k(TURBO_TOP_K if self.is_turbo else MTL_TOP_K)
+        self.gguf_writer.add_sampling_min_p(sampling["min_p"])
+        self.gguf_writer.add_sampling_top_p(sampling["top_p"])
+        self.gguf_writer.add_sampling_temp(sampling["temp"])
+        self.gguf_writer.add_sampling_penalty_repeat(sampling["penalty_repeat"])
+        self.gguf_writer.add_sampling_penalty_last_n(PENALTY_LAST_N)
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        if self.is_turbo:
+            return
+        # llama3 rope scaling baked into the rope_freqs factors tensor
+        rp = self.hparams["rope_scaling"]
+        dim = self.hparams["head_dim"]
+        base = self.hparams["rope_theta"]
+        freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+        factor = rp["factor"]
+        low_freq_wavelen = rp["original_max_position_embeddings"] / rp["low_freq_factor"]
+        high_freq_wavelen = rp["original_max_position_embeddings"] / rp["high_freq_factor"]
+        rope_factors = []
+        for freq in freqs:
+            wavelen = 2 * math.pi / freq
+            if wavelen < high_freq_wavelen:
+                rope_factors.append(1)
+            elif wavelen > low_freq_wavelen:
+                rope_factors.append(factor)
+            else:
+                smooth = (rp["original_max_position_embeddings"] / wavelen - rp["low_freq_factor"]) / (rp["high_freq_factor"] - rp["low_freq_factor"])
+                rope_factors.append(1 / ((1 - smooth) / factor + smooth))
+        yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
+
+    def _maybe_emit_fused(self) -> Iterable[tuple[str, Tensor]]:
+        if self._text_embd is not None and self._speech_embd is not None:
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD),
+                   torch.cat([self._text_embd, self._speech_embd], dim=0))
+            self._text_embd = None
+            self._speech_embd = None
+        if self._text_head is not None and self._speech_head is not None:
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT),
+                   torch.cat([self._text_head, self._speech_head], dim=0))
+            self._text_head = None
+            self._speech_head = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # generate_extra_tensors output comes back through here with its final name
+        if name.startswith("rope_freqs"):
+            yield (name, data_torch)
+            return
+        # fused [text | speech] vocab: embeddings and output head
+        if name == "text_emb.weight":
+            if not self.is_turbo:
+                # the reference lowercases its input, the uppercase rows take the lowercase ones
+                fold = list(range(data_torch.shape[0]))
+                for upper, lower in self._mtl_case_fold_pairs():
+                    fold[upper] = lower
+                data_torch = data_torch[torch.tensor(fold)]
+            self._text_embd = data_torch
+            yield from self._maybe_emit_fused()
+            return
+        if name == "speech_emb.weight":
+            self._speech_embd = data_torch
+            yield from self._maybe_emit_fused()
+            return
+        if name == "text_head.weight":
+            self._text_head = data_torch
+            yield from self._maybe_emit_fused()
+            return
+        if name == "speech_head.weight":
+            self._speech_head = data_torch
+            yield from self._maybe_emit_fused()
+            return
+        if name == "speech_head.bias":
+            # the text head has no bias, its zone is suppressed at sampling anyway
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT, suffix=".bias"), F.pad(data_torch, (self.hparams["vocab_size"], 0)))
+            return
+
+        assert name.startswith("tfmr.")
+        name = name[len("tfmr."):]
+
+        if self.is_turbo:
+            # HF GPT-2 layout: Conv1D style weights are stored transposed
+            if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight")):
+                data_torch = data_torch.transpose(1, 0)
+            yield (self.map_tensor_name(name), data_torch)
+            return
+
+        if name.endswith("q_proj.weight"):
+            data_torch = LlamaModel.permute(data_torch, self.hparams["num_attention_heads"], None)
+        if name.endswith("k_proj.weight"):
+            data_torch = LlamaModel.permute(data_torch, self.hparams["num_attention_heads"], self.hparams["num_key_value_heads"])
+        yield (self.map_tensor_name("model." + name), data_torch)
+
+
+@ModelBase.register("ChatterboxModel")
+class ChatterboxMmprojModel(MmprojModel):
+    has_vision_encoder = False
+    has_audio_encoder = True
+
+    def __init__(self, dir_model: Path, *args, **kwargs):
+        self.is_turbo = _is_turbo(dir_model)
+        super().__init__(dir_model, *args, **kwargs)
+        self._wnorm_g: dict[str, Tensor] = {}
+        self._wnorm_v: dict[str, Tensor] = {}
+
+    def get_audio_config(self) -> dict[str, Any] | None:
+        return self.global_config.get("audio_config")
+
+    def index_tensors(self, remote_hf_model_id: str | None = None) -> dict[str, Callable[[], Tensor]]:
+        tensors: dict[str, Callable[[], Tensor]] = {}
+        for prefix in (TURBO_S3GEN if self.is_turbo else MTL_S3GEN, VE, TURBO_TALKER if self.is_turbo else MTL_TALKER):
+            tensors.update(self.index_local_tensors(prefix))
+        return tensors
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        # the s3gen, ve and talker checkpoints have disjoint top-level names
+        name, gen = item
+        new_name: str | None = None
+        # batchnorm bookkeeping (over the gguf name length cap) and the tokenizer dsp buffers are unused
+        if name.endswith("num_batches_tracked") or name in ("tokenizer.window", "tokenizer._mel_filters"):
+            return None
+        for src, dst in (
+            ("flow.encoder.",           "a.gen.fenc."),
+            ("flow.decoder.estimator.", "a.gen.est."),
+            ("mel2wav.",                "a.gen.hift."),
+            ("speaker_encoder.",        "a.spk."),
+            ("tokenizer.",              "a.s3tok."),
+            ("flow.",                   "a.gen.flow."),  # input_embedding, encoder_proj, spk_embed_affine_layer
+            ("lstm.",                   "a.ve.lstm."),
+            ("proj.",                   "a.ve.proj."),
+            ("cond_enc.",               "a.cenc."),
+        ):
+            if name.startswith(src):
+                new_name = dst + name[len(src):]
+                break
+        if name == "text_pos_emb.emb.weight":
+            new_name = "a.gen.t3.text_pos_emb"
+        elif name == "speech_pos_emb.emb.weight":
+            new_name = "a.gen.t3.speech_pos_emb"
+        elif name == "speech_emb.weight":
+            new_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.A_GEN_CODE_OUT_EMBD] + ".weight"
+        if new_name is None:
+            return None
+        return super().filter_tensors((new_name, gen))
+
+    def set_gguf_parameters(self):
+        self.gguf_writer.add_file_type(self.ftype)
+
+        # speaker encoder, the front-end hparams are hard-coded in clip.cpp
+        self.gguf_writer.add_clip_has_audio_encoder(True)
+        self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.CHATTERBOX_SPKENC)
+        self.gguf_writer.add_audio_projection_dim(80)
+        self.gguf_writer.add_audio_num_mel_bins(80)
+        self.gguf_writer.add_audio_block_count(0)
+        self.gguf_writer.add_audio_embedding_length(192)
+        self.gguf_writer.add_audio_head_count(1)
+        self.gguf_writer.add_audio_feed_forward_length(192)
+        self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
+
+        # audio generator (s3gen, chatterbox projector); the flow encoder shape
+        self.gguf_writer.add_clip_has_gen_audio_encoder(True)
+        self.gguf_writer.add_clip_gen_audio_projector_type(gguf.VisionProjectorType.CHATTERBOX)
+        self.gguf_writer.add_gen_audio_projection_dim(80)
+        self.gguf_writer.add_gen_audio_embedding_length(512)
+        self.gguf_writer.add_gen_audio_feed_forward_length(2048)
+        self.gguf_writer.add_gen_audio_block_count(6)
+        self.gguf_writer.add_gen_audio_head_count(8)
+        self.gguf_writer.add_gen_audio_attention_layernorm_eps(1e-5)
+
+    def _fuse_weight_norm(self, name: str, data_torch: Tensor) -> tuple[str, Tensor] | None:
+        # torch weight_norm parametrization: weight = g * v / |v| over dims 1..n
+        base = name.split(".parametrizations.weight.original")[0]
+        if name.endswith("original0"):
+            self._wnorm_g[base] = data_torch
+        else:
+            self._wnorm_v[base] = data_torch
+        if base in self._wnorm_g and base in self._wnorm_v:
+            g = self._wnorm_g.pop(base)
+            v = self._wnorm_v.pop(base)
+            norm = v.float().norm(dim=tuple(range(1, v.dim())), keepdim=True)
+            return (base + ".weight", g.float() * v.float() / norm)
+        return None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if ".parametrizations.weight.original" in name:
+            fused = self._fuse_weight_norm(name, data_torch)
+            if fused is not None:
+                yield fused
+            return
+        yield (name, data_torch)
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        del name, bid
+        # tensors read on the host stay F32
+        if new_name.startswith(("a.ve.", "a.cenc.", "voice.", "a.gen.t3.", "a.gen.hift.m_source.")):
+            return gguf.GGMLQuantizationType.F32
+        # the conv ops have no BF16 kernels
+        if n_dims >= 2 and new_name.endswith((".weight", ".weight_v")):
+            return gguf.GGMLQuantizationType.F16
+        return gguf.GGMLQuantizationType.F32
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        conds = torch.load(self.dir_model / "conds.pt", map_location="cpu", weights_only=True)
+        t3c = conds["t3"] if isinstance(conds, dict) else conds.t3
+        genc = conds["gen"] if isinstance(conds, dict) else conds.gen
+        t3c = vars(t3c) if not isinstance(t3c, dict) else t3c
+        genc = vars(genc) if not isinstance(genc, dict) else genc
+
+        def talker_tensor(name: str) -> Tensor:
+            return LazyTorchTensor.to_eager(self.model_tensors[name]())
+
+        # the conds.pt voice: voice.default holds the talker conditioning rows, voice.default.ref_* the flow decoder reference
+        # 1D tensors are written as F32, the speech ids stay exact
+        yield ("voice.default.ref_tokens", genc["prompt_token"][0].float())
+        yield ("voice.default.ref_feat", genc["prompt_feat"][0].float())
+        yield ("voice.default.ref_spk", genc["embedding"][0].float())
+
+        spkr_w = talker_tensor("a.cenc.spkr_enc.weight").float()
+        spkr_b = talker_tensor("a.cenc.spkr_enc.bias").float()
+        spkr_row = spkr_w @ t3c["speaker_emb"][0].float() + spkr_b
+        speech_emb = talker_tensor(self.format_tensor_name(gguf.MODEL_TENSOR.A_GEN_CODE_OUT_EMBD)).float()
+        cond_tokens = t3c["cond_prompt_speech_tokens"][0]
+
+        if self.is_turbo:
+            # turbo talker conditioning: [spkr, speech rows of the cond tokens]
+            yield ("voice.default", torch.cat([spkr_row[None], speech_emb[cond_tokens]], dim=0))
+            return
+
+        # multilingual talker conditioning [spkr, perceiver x32, emotion], from the reference perceiver over the cond speech tokens
+        with torch.no_grad():
+            speech_pos = talker_tensor("a.gen.t3.speech_pos_emb").float()
+            pse = speech_emb[cond_tokens] + speech_pos[: cond_tokens.shape[0]]
+
+            ln_w = talker_tensor("a.cenc.perceiver.attn.norm.weight").float()
+            ln_b = talker_tensor("a.cenc.perceiver.attn.norm.bias").float()
+            wq = talker_tensor("a.cenc.perceiver.attn.to_q.weight").float()
+            bq = talker_tensor("a.cenc.perceiver.attn.to_q.bias").float()
+            wk = talker_tensor("a.cenc.perceiver.attn.to_k.weight").float()
+            bk = talker_tensor("a.cenc.perceiver.attn.to_k.bias").float()
+            wv = talker_tensor("a.cenc.perceiver.attn.to_v.weight").float()
+            bv = talker_tensor("a.cenc.perceiver.attn.to_v.bias").float()
+            wo = talker_tensor("a.cenc.perceiver.attn.proj_out.weight").float()
+            bo = talker_tensor("a.cenc.perceiver.attn.proj_out.bias").float()
+            query = talker_tensor("a.cenc.perceiver.pre_attention_query")[0].float()
+
+            n_head = 4
+            n_e = query.shape[1]
+
+            def attn_block(x1: Tensor, x2: Tensor) -> Tensor:
+                nx1 = F.layer_norm(x1, (n_e,), ln_w, ln_b)
+                nx2 = F.layer_norm(x2, (n_e,), ln_w, ln_b)
+                q = (nx1 @ wq.T + bq).view(-1, n_head, n_e // n_head).transpose(0, 1)
+                k = (nx2 @ wk.T + bk).view(-1, n_head, n_e // n_head).transpose(0, 1)
+                v = (nx2 @ wv.T + bv).view(-1, n_head, n_e // n_head).transpose(0, 1)
+                ctx = F.scaled_dot_product_attention(q, k, v)
+                ctx = ctx.transpose(0, 1).reshape(-1, n_e)
+                return ctx @ wo.T + bo + x1
+
+            pre = attn_block(query, pse)
+            p32 = attn_block(pre, pre)
+
+            emo = talker_tensor("a.cenc.emotion_adv_fc.weight").float()
+            emo_row = emo[:, 0] * t3c["emotion_adv"].reshape(-1)[0]
+            yield ("voice.default", torch.cat([spkr_row[None], p32, emo_row[None]], dim=0))
@@ -0,0 +1,41 @@
+from __future__ import annotations
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+from .base import ModelBase, gguf
+from .qwen import _LinearAttentionVReorderBase
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+
+@ModelBase.register("CloverMoeModelLM")
+@ModelBase.example("Diffuspace/Clover-35B-A3B-Preview")
+class CloverMoeModel(_LinearAttentionVReorderBase):
+    model_arch = gguf.MODEL_ARCH.CLOVER_MOE
+    supports_mtp_export = False
+
+    def __init__(self, *args, **kwargs):
+        self.no_mtp = True
+        super().__init__(*args, **kwargs)
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_expert_shared_count(self.hparams.get("num_shared_experts", 1))
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, _ = item
+        if name.endswith((".conv_right_weight", ".bd_softmax_logit_scale", ".bd_softmax_history_bias", ".bd_softmax_history_c")):
+            return None
+
+        return super().filter_tensors(item)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.endswith(".A_log") or (name.endswith("norm.weight") and not name.endswith("linear_attn.norm.weight")):
+            data_torch = data_torch.float()
+
+        yield from super().modify_tensors(data_torch, name, bid)
@@ -236,6 +236,12 @@ class DeepseekModel(TextModel):
 class DeepseekV2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.DEEPSEEK2

+    def _map_fp8_weight_names(self, name: str) -> tuple[str, ...]:
+        # MLA projections do not have runtime weight-scale support.
+        if name.endswith((".q_a_proj.weight", ".q_b_proj.weight", ".kv_a_proj_with_mqa.weight", ".kv_b_proj.weight")):
+            return ()
+        return super()._map_fp8_weight_names(name)
+
    # TODO @ngxson : remove this when we support MTP for deepseek models
    skip_mtp = True

@@ -453,9 +453,6 @@ class LFM3AudioMmprojModel(MmprojModel):
    def tensor_force_quant(self, name, new_name, bid, n_dims):
        if new_name.startswith("a.conv1d.") or "conv_dw" in new_name or new_name.startswith("a.gen.wav.quant."):
            return gguf.GGMLQuantizationType.F32
-        # the codec decoder is small and its log-magnitude/phase output does not survive quantization
-        if new_name.startswith("a.gen.wav."):
-            return gguf.GGMLQuantizationType.F16
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
@@ -0,0 +1,184 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("LimiteForCausalLM")
+@ModelBase.example("paradigma-inc/limite-1b-violetto")
+class LimiteModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.LIMITE
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        hp = self.hparams
+
+        expected = {
+            "pos_mode": "rope",
+            "qk_norm": "rms_pre_rope",
+            "rope_style": "interleaved_pairs_odd_lane_sign_flip",
+            "rope_per_layer": False,
+            "rms_norm_has_weight": False,
+            "rms_norm_eps_mode": "torch_finfo_default",
+            "mlp_type": "swiglu",
+            "attn_gate_applied": "per_head_before_o_proj",
+            "ve_head_slice": "first_num_key_value_heads",
+            "ve_applied_before_qk_norm": True,
+            "global_nope": True,
+            "global_window": -1,
+            "mudd_r_site": "resid",
+            "tie_word_embeddings": True,
+            "final_softcap": 0.0,
+        }
+        for key, value in expected.items():
+            if hp.get(key) != value:
+                raise NotImplementedError(f"Limite: unsupported {key}={hp.get(key)!r}, expected {value!r}")
+        if hp["softcap_logits"]["kind"] != "sigmoid":
+            raise NotImplementedError(f"Limite: unsupported softcap_logits kind {hp['softcap_logits']['kind']!r}")
+
+        self.n_head    = hp["num_attention_heads"]
+        self.n_head_kv = hp["num_key_value_heads"]
+        self.head_dim  = hp["head_dim"]
+        self.n_pairs   = hp["rope_n_pairs"]
+
+        self.mudd_taps: dict[int, list[int]] = {}
+        if hp.get("mudd"):
+            if not hp.get("mudd_mlp"):
+                raise NotImplementedError("Limite: mudd without mudd_mlp is not supported")
+            self.mudd_taps = {int(il): [int(t) for t in taps] for il, taps in hp["mudd_tap_idx"].items()}
+            assert all(len(taps) == hp["mudd_taps"] for taps in self.mudd_taps.values())
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hp = self.hparams
+
+        self.gguf_writer.add_key_length(self.head_dim)
+        self.gguf_writer.add_value_length(self.head_dim)
+
+        # torch rms_norm with eps=None uses the fp32 epsilon, even for bf16 inputs
+        self.gguf_writer.add_layer_norm_rms_eps(torch.finfo(torch.float32).eps)
+        self.gguf_writer.add_attention_scale(hp["attention_softmax_scale"])
+        self.gguf_writer.add_xsa_eps(hp["xsa_normalize_eps"] if hp.get("xsa") else 0.0)
+
+        # HF freq_i = base^(-i/(n_pairs-1)), ggml uses freq_base^(-i/n_pairs)
+        self.gguf_writer.add_rope_dimension_count(2 * self.n_pairs)
+        self.gguf_writer.add_rope_freq_base(hp["rope_base_local"] ** (self.n_pairs / (self.n_pairs - 1)))
+
+        # HF keeps keys k >= q - window, so a window of W spans W + 1 tokens
+        global_layers = set(hp["global_layers"])
+        self.gguf_writer.add_sliding_window(hp["sliding_window"] + 1)
+        self.gguf_writer.add_sliding_window_pattern([il not in global_layers for il in range(self.block_count)])
+
+        cap = hp["softcap_logits"]
+        self.gguf_writer.add_final_logit_sigmoid_capping(cap["a"], cap["b"], cap["c"])
+
+        if self.mudd_taps:
+            n_taps = hp["mudd_taps"]
+            tap_indices = [-1] * (self.block_count * n_taps)
+            for il, taps in self.mudd_taps.items():
+                tap_indices[il * n_taps:(il + 1) * n_taps] = taps
+            self.gguf_writer.add_mudd_feed_forward_length(hp["mudd_inter"])
+            self.gguf_writer.add_mudd_tap_count(n_taps)
+            self.gguf_writer.add_mudd_tap_indices(tap_indices)
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        # tiny mixing weights, kept at full precision as in the reference
+        # the mudd tensors come from a global source tensor, so take the block id from the new name
+        if new_name.startswith("blk."):
+            bid = int(new_name.split(".")[1])
+        for key in (gguf.MODEL_TENSOR.ATTN_GATE, gguf.MODEL_TENSOR.ATTN_VE_GATE, gguf.MODEL_TENSOR.MUDD_DOWN, gguf.MODEL_TENSOR.MUDD_UP):
+            if self.match_model_tensor_name(new_name, key, bid):
+                return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    def _swap_rope_pairs(self, w: Tensor, n_head: int) -> Tensor:
+        # HF rotates each pair by -theta; swapping the pair lanes gives the ggml +theta rotation.
+        # q and k are both swapped, so the attention scores do not change.
+        idx = torch.arange(self.head_dim)
+        idx[:2 * self.n_pairs] = idx[:2 * self.n_pairs].view(-1, 2).flip(-1).reshape(-1)
+        w = w.reshape(n_head, self.head_dim, -1)
+        return w[:, idx, :].reshape(n_head * self.head_dim, -1)
+
+    def _pad_cols(self, w: Tensor) -> Tensor:
+        # gates read only the first channels of the input, pad them to n_embd
+        return torch.nn.functional.pad(w, (0, self.hparams["hidden_size"] - w.shape[-1]))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        hp = self.hparams
+        T = gguf.MODEL_TENSOR
+
+        def load(n: str) -> Tensor:
+            return self.model_tensors[n]().float()
+
+        if name == "model.value_embeds.weight":
+            ve = data_torch.reshape(data_torch.shape[0], -1, hp["ve_dim"])[:, :self.n_head_kv]
+            ve = torch.nn.functional.pad(ve, (0, self.head_dim - hp["ve_dim"]))
+            yield self.format_tensor_name(T.VALUE_EMBD), ve.reshape(ve.shape[0], -1) * hp["ve_gate_scale"]
+            return
+
+        if name == "model.mudd.dense1":
+            for il in self.mudd_taps:
+                yield self.format_tensor_name(T.MUDD_DOWN, il), data_torch
+            return
+        if name == "model.mudd.dense2":
+            # rows [0, n) mix the attention input, rows [n, 2n) mix the residual
+            dense2_mlp = load("model.mudd.dense2_mlp")
+            bias       = load("model.mudd.bias")
+            bias_mlp   = load("model.mudd.bias_mlp")
+            for il, taps in self.mudd_taps.items():
+                n = len(taps)
+                w = torch.cat([data_torch[il, :n], dense2_mlp[il, :n]], 0)
+                b = torch.cat([bias[il, :n], bias_mlp[il, :n]], 0)
+                yield self.format_tensor_name(T.MUDD_UP, il), w
+                yield self.format_tensor_name(T.MUDD_UP, il, ".bias"), b
+            return
+        if name.startswith("model.mudd."):
+            return
+
+        if bid is None:
+            yield from super().modify_tensors(data_torch, name, bid)
+            return
+
+        prefix = f"model.layers.{bid}."
+        name = name.removeprefix(prefix)
+
+        if name in ("self_attn.q_proj.weight", "self_attn.k_proj.weight", "self_attn.v_proj.weight"):
+            data_torch = data_torch * load(prefix + "self_attn.qkv_scale")
+            if name == "self_attn.q_proj.weight":
+                data_torch = self._swap_rope_pairs(data_torch, self.n_head)
+            elif name == "self_attn.k_proj.weight":
+                data_torch = self._swap_rope_pairs(data_torch, self.n_head_kv)
+        elif name == "self_attn.o_proj.weight":
+            data_torch = data_torch * load(prefix + "self_attn.o_scale") * load(prefix + "post_lambda_attn") * hp["attn_gate_scale"]
+        elif name == "mlp.down_proj.weight":
+            data_torch = data_torch * load(prefix + "post_lambda_mlp")
+        elif name == "self_attn.attn_gate":
+            yield self.format_tensor_name(T.ATTN_GATE, bid), self._pad_cols(data_torch)
+            return
+        elif name == "self_attn.ve_gate":
+            yield self.format_tensor_name(T.ATTN_VE_GATE, bid), self._pad_cols(data_torch[:self.n_head_kv])
+            return
+        elif name == "self_attn.xsa_alpha":
+            if hp.get("xsa") and bid in hp["xsa_layers"]:
+                yield self.format_tensor_name(T.ATTN_XSA_ALPHA, bid), torch.tanh(data_torch)
+            return
+        elif name == "resid_lambda_attn":
+            yield self.format_tensor_name(T.ATTN_RESID_SCALE, bid), data_torch.reshape(1)
+            return
+        elif name == "resid_lambda_mlp":
+            yield self.format_tensor_name(T.FFN_RESID_SCALE, bid), data_torch.reshape(1)
+            return
+        elif name in ("self_attn.qkv_scale", "self_attn.o_scale", "post_lambda_attn", "post_lambda_mlp"):
+            # folded into the projections above
+            return
+
+        yield from super().modify_tensors(data_torch, prefix + name, bid)
@@ -0,0 +1,204 @@
+from __future__ import annotations
+
+import math
+from typing import Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import LazyTorchTensor, ModelBase, TextModel, gguf
+
+
+@ModelBase.register("Metis16ForCausalLM")
+class Metis16Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.METIS16
+
+    # read by generate_extra_tensors() or not used at inference: dropped in modify_tensors()
+    _folded = {
+        "pass_embeddings", "lm_head.weight", "depth_budget.multiplier",
+        "depth_memory.pass_embeddings", "depth_memory.anchor_embeddings",
+        "depth_memory.state_write.bias", "depth_memory.metadata_write.bias",
+    }
+    _folded_layer = {
+        "mixer_connection.controller.weight", "mixer_connection.controller.bias", "mixer_connection.mix_logits",
+        "mixer_connection.read_logits", "mixer_connection.write_logits",
+        "moe_connection.controller.weight", "moe_connection.controller.bias", "moe_connection.mix_logits",
+        "moe_connection.read_logits", "moe_connection.write_logits",
+        "moe.k_router.weight", "moe.k_router.bias", "moe.k_budget.multiplier",
+    }
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        hp = self.hparams
+        self.n_pass = hp["num_passes"]
+        self.attn_layers = list(hp["attention_layer_indices"])
+        self._generated: set[str] = set()
+        # one cache slot per pass and layer, while the tensors exist once per physical layer
+        self.block_count = self.n_pass * hp["num_hidden_layers"]
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+        # turns end on the first eos id, so llama.cpp must stop on it too
+        eos = self.hparams.get("eos_token_id")
+        if isinstance(eos, list) and eos:
+            self.gguf_writer.add_eot_token_id(eos[0])
+
+    def set_gguf_parameters(self):
+        hp = self.hparams
+        super().set_gguf_parameters()
+
+        n_layer = hp["num_hidden_layers"]
+        self.gguf_writer.add_head_count_kv([hp["num_key_value_heads"] if il % n_layer in self.attn_layers else 0 for il in range(self.block_count)])
+
+        self.gguf_writer.add_expert_feed_forward_length(hp["expert_intermediate_size"])
+        self.gguf_writer.add_expert_shared_feed_forward_length(hp["shared_expert_intermediate_size"])
+        self.gguf_writer.add_expert_shared_count(1)
+        self.gguf_writer.add_moe_latent_size(hp["latent_dim"])
+
+        d_inner = hp["mamba_expand"] * hp["hidden_size"]
+        self.gguf_writer.add_ssm_conv_kernel(hp["mamba_d_conv"])
+        self.gguf_writer.add_ssm_inner_size(d_inner)
+        self.gguf_writer.add_ssm_state_size(hp["mamba_d_state"])
+        self.gguf_writer.add_ssm_time_step_rank(d_inner // hp["mamba_head_dim"])
+        self.gguf_writer.add_ssm_group_count(hp["mamba_n_groups"])
+        self.gguf_writer.add_ssm_norm_epsilon(hp["mamba_norm_eps"])
+
+        self.gguf_writer.add_hyper_connection_count(hp["n_streams"])
+        self.gguf_writer.add_metis_pass_count(self.n_pass)
+        self.gguf_writer.add_metis_memory_length(hp["memory_dim"])
+        self.gguf_writer.add_metis_route_length(hp["route_feature_dim"])
+        self.gguf_writer.add_metis_continuation_logit_limit(hp["continuation_logit_limit"])
+        self.gguf_writer.add_attention_pass_lora_rank(hp["attention_pass_lora_rank"])
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+        # the checkpoint leaves these names suffix-less; add the suffix of the GGUF tensor so the mapper keeps it
+        if name.endswith((".mixer.impl.mixer.dt_bias", ".moe.selection_bias")):
+            name += ".bias"
+        elif (name.endswith((".moe.expert_embeddings", ".mixer.pass_gates"))
+              or name in ("depth_memory.stream_gate", "stream_embeddings")):
+            name += ".weight"
+        elif name == "depth_memory.stream_gate_bias":
+            name = "depth_memory.stream_gate.bias"
+        return super().filter_tensors((name, gen))
+
+    def _load(self, name: str) -> Tensor:
+        return LazyTorchTensor.to_eager(self.model_tensors[name]())
+
+    def _emit(self, name: str, data: Tensor) -> tuple[str, Tensor]:
+        self._generated.add(name)
+        return name, data
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        hp = self.hparams
+        r = hp["route_feature_dim"]
+        n_stream = hp["n_streams"]
+        T = gguf.MODEL_TENSOR
+        passes = self._load("pass_embeddings")[: self.n_pass]
+
+        # the mixes depend only on the weights and the pass, so Sinkhorn runs once here
+        # the controller runs in the checkpoint dtype, as the reference does
+        for il in range(hp["num_hidden_layers"]):
+            for src, (mix, read, write) in (
+                ("mixer_connection", (T.HC_ATTN_MIX, T.HC_ATTN_READ, T.HC_ATTN_WRITE)),
+                ("moe_connection", (T.HC_FFN_MIX, T.HC_FFN_READ, T.HC_FFN_WRITE)),
+            ):
+                prefix = f"layers.{il}.{src}."
+                ctrl_w, ctrl_b = self._load(prefix + "controller.weight"), self._load(prefix + "controller.bias")
+                mixes, reads, writes = [], [], []
+                for p in range(self.n_pass):
+                    delta = torch.nn.functional.linear(passes[p].to(ctrl_w.dtype), ctrl_w, ctrl_b).float()
+                    m_delta, r_delta, w_delta = torch.split(delta, [n_stream * n_stream, n_stream, n_stream])
+                    log_m = self._load(prefix + "mix_logits").float() + m_delta.view(n_stream, n_stream)
+                    for _ in range(hp["mhc_sinkhorn_iterations"]):
+                        log_m = log_m - torch.logsumexp(log_m, dim=-1, keepdim=True)
+                        log_m = log_m - torch.logsumexp(log_m, dim=-2, keepdim=True)
+                    mixes.append(log_m.exp())
+                    reads.append(torch.softmax(self._load(prefix + "read_logits").float() + r_delta, dim=-1))
+                    writes.append(torch.softmax(self._load(prefix + "write_logits").float() + w_delta, dim=-1))
+                act = ctrl_w.dtype
+                yield self._emit(self.format_tensor_name(mix, il), torch.stack(mixes).to(act).float())
+                yield self._emit(self.format_tensor_name(read, il), torch.stack(reads).to(act).float())
+                yield self._emit(self.format_tensor_name(write, il), torch.stack(writes).to(act).float())
+
+        # one bias per memory entry: write biases, pass and anchor embeddings, and the constant routed width
+        meta_w = self._load("depth_memory.metadata_write.weight").float()
+        mean_k = hp["num_experts_per_tok"] / hp["max_routed_k"]
+        base = (self._load("depth_memory.state_write.bias").float() + self._load("depth_memory.metadata_write.bias").float()
+                + mean_k * meta_w[:, r])
+        pass_e = self._load("depth_memory.pass_embeddings").float()
+        anchor_e = self._load("depth_memory.anchor_embeddings").float()
+        n_anchor = len(self.attn_layers) + 1
+        entry_bias = torch.stack([base + pass_e[p] + anchor_e[a] for p in range(self.n_pass) for a in range(n_anchor)])
+        yield self._emit(self.format_tensor_name(T.DEPTH_MEM_ENTRY_BIAS), entry_bias)
+
+    def _split_features(self, data_torch: Tensor, template: str, parts: tuple[str, ...], d: int, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # the graph reads the concatenated features one block at a time; each block is its own tensor
+        for i, part in enumerate(parts):
+            piece = data_torch[:, i * d:(i + 1) * d] if i < 3 else data_torch[:, 3 * d:]
+            yield from super().modify_tensors(piece, template.format(part), bid)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name in self._generated:
+            yield name, data_torch
+            return
+        if name in self._folded:
+            return
+        hp = self.hparams
+        d = hp["hidden_size"]
+
+        if bid is None:
+            if name == "stream_embeddings.weight":
+                # the graph keeps the streams innermost: [n_stream, n_embd, n_tokens]
+                yield from super().modify_tensors(data_torch.T.contiguous(), name, bid)
+                return
+            if name == "depth_memory.metadata_write.weight":
+                # metadata input becomes [summary, entropy, confidence, continuation]; entropy arrives unnormalized
+                r = hp["route_feature_dim"]
+                meta_w = data_torch.float()
+                entropy_col = meta_w[:, r + 1:r + 2] / math.log(hp["n_routed_experts"])
+                data_torch = torch.cat((meta_w[:, :r], entropy_col, meta_w[:, r + 2:]), dim=1)
+            elif name == "depth_memory.stream_gate.weight":
+                data_torch = data_torch.float() / math.sqrt(d)
+            elif name == "depth_memory.route_projection.weight":
+                yield from self._split_features(data_torch, "depth_memory.route_{}.weight", ("state", "retr", "diff", "hist"), d, bid)
+                return
+            elif name == "continuation.hidden.weight":
+                yield from self._split_features(data_torch, "continuation.hidden_{}.weight", ("state", "retr", "diff", "route"), d, bid)
+                return
+            yield from super().modify_tensors(data_torch, name, bid)
+            return
+
+        local = name.removeprefix(f"layers.{bid}.")
+        if local in self._folded_layer:
+            return
+
+        if local == "mixer.impl.mixer.A_log":
+            data_torch = -torch.exp(data_torch).unsqueeze(-1)
+        elif local == "mixer.impl.mixer.D":
+            data_torch = data_torch.unsqueeze(-1)
+        elif local == "mixer.impl.mixer.conv1d.weight":
+            data_torch = data_torch.squeeze(1)
+        elif local == "mixer.impl.mixer.norm.weight":
+            data_torch = data_torch.reshape(hp["mamba_n_groups"], -1)
+        elif local == "mixer.pass_gates.weight":
+            # the reference applies tanh(gate) in the checkpoint dtype
+            data_torch = torch.tanh(data_torch[: self.n_pass]).to(torch.bfloat16).float()
+        elif local == "moe.shared_expert.gate_up.weight":
+            gate, up = data_torch.chunk(2, dim=0)
+            yield from super().modify_tensors(gate, name.replace("gate_up", "gate"), bid)
+            yield from super().modify_tensors(up, name.replace("gate_up", "up"), bid)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
+        # FP32 in the checkpoint, tiny and read as rows, or computed here at full precision: keep FP32
+        if (new_name.startswith(("cont_", "depth_mem_entry_bias", "depth_mem_stream_gate"))
+                or new_name.endswith(("ffn_exp_embd.weight", "ffn_gate_inp.weight", "hc_attn_mix.weight", "hc_attn_read.weight",
+                                      "hc_attn_write.weight", "hc_ffn_mix.weight", "hc_ffn_read.weight", "hc_ffn_write.weight"))):
+            return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
@@ -0,0 +1,217 @@
+from __future__ import annotations
+
+from typing import Callable, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, gguf
+
+from .mistral import MistralModel, MistralMoeModel
+
+
+@ModelBase.register("MistralLarge4ForConditionalGeneration")
+class MistralLarge4Model(MistralMoeModel):
+    model_arch = gguf.MODEL_ARCH.MISTRAL_LARGE4
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        n_layer = self.hparams["num_hidden_layers"]
+        dsa = self.hparams["dsa"]
+
+        def expand(pattern: str) -> list[str]:
+            values = pattern.split(",")
+            return [values[i % len(values)] for i in range(n_layer)]
+
+        self.layer_has_rope = [v == "rope" for v in expand(self.hparams.get("nope") or "rope")]
+        self.layer_indexer_full = [v == "full" for v in expand(dsa.get("sharing") or "full")]
+
+        kv_sharing = self.hparams.get("kv_sharing") or {}
+        if "source_layers_str" in kv_sharing:
+            self.layer_kv_source = [int(v) for v in kv_sharing["source_layers_str"].split(",")]
+        else:
+            assert not kv_sharing, f"unsupported kv_sharing config: {kv_sharing}"
+            self.layer_kv_source = list(range(n_layer))
+        assert len(self.layer_kv_source) == n_layer
+
+        # note: the rope dim of the indexer must match the main attention
+        assert dsa.get("rope_head_dim", -1) in (-1, self.hparams["qk_rope_head_dim"])
+        assert dsa.get("match_nope", False), "indexer must follow the NoPE pattern of the main attention"
+        assert dsa.get("ssmax_scaling", False), "indexer without SSMax is not supported"
+        assert self.hparams.get("q_lora_rank") is None
+        assert not self.hparams["yarn"].get("apply_scale", True), "only YaRN without magnitude scaling is supported"
+
+    def dequant_model(self):
+        quant_config = self.hparams.get("quantization")
+        if quant_config is not None:
+            assert quant_config["qformat_weight"] == "fp8_e4m3"
+            assert quant_config.get("qscheme_weight") == "BLOCK"
+            self.hparams["quantization_config"] = {
+                "quant_method": "fp8",
+                "weight_block_size": [quant_config["block_size_out"], quant_config["block_size_in"]],
+            }
+        return super().dequant_model()
+
+    def set_gguf_parameters(self):
+        # skip the MistralMoeModel overrides, they apply the deepseek2 YaRN scaling
+        super(MistralMoeModel, self).set_gguf_parameters()
+        MistralModel.set_mistral_config(self.gguf_writer, self.hparams)
+
+        dsa = self.hparams["dsa"]
+        self.gguf_writer.add_indexer_head_count(dsa["n_heads"])
+        self.gguf_writer.add_indexer_key_length(dsa["head_dim"])
+        self.gguf_writer.add_indexer_top_k(dsa["topk"])
+        self.gguf_writer.add_indexer_types(self.layer_indexer_full)
+        self.gguf_writer.add_layer_norm_eps(dsa.get("norm_eps", 1e-6))
+
+        self.gguf_writer.add_rope_pattern(self.layer_has_rope)
+        self.gguf_writer.add_kv_source_map(self.layer_kv_source)
+        self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
+        self.gguf_writer.add_final_logit_softcapping(self.hparams["softmax_tanh_gating"])
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # exported separately with --dspark
+        if name.startswith("mtp_"):
+            return None
+
+        # experts need HF names for the merging logic in DeepseekV2Model
+        # note: bare "layers.N.experts" comes from the NVFP4 quantized_layers config
+        if ".experts." in name or name.endswith(".experts"):
+            name = "model." + name.replace(".experts", ".mlp.experts")
+            name = name.replace(".w1.", ".gate_proj.").replace(".w2.", ".down_proj.").replace(".w3.", ".up_proj.")
+
+        # skip the MistralMoeModel renaming, the qscale names are needed for block FP8 dequant
+        return super(MistralMoeModel, cls).filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name == "lerope.alpha":
+            # LeRoPE scales inv_freq by exp(alpha), ggml divides theta by the freq factors
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.exp(-data_torch.float()))
+            return
+
+        if bid is not None and name.endswith((".ssmax_scaling.s", ".ssmax_scaling.b")):
+            tensor = gguf.MODEL_TENSOR.INDEXER_SSMAX if ".indexer." in name else gguf.MODEL_TENSOR.ATTN_SSMAX
+            suffix = ".weight" if name.endswith(".s") else ".bias"
+            yield (self.format_tensor_name(tensor, bid, suffix), data_torch.float())
+            return
+
+        if bid is not None and ".indexer.q_input_norm." in name:
+            suffix = ".weight" if name.endswith(".weight") else ".bias"
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.INDEXER_Q_NORM, bid, suffix), data_torch)
+            return
+
+        if bid is not None and ".indexer." in name:
+            name = "model." + name.replace(".attention.", ".self_attn.")
+
+        # NoPE layers have a larger non-rotary head, so wkv_b is split per layer
+        if bid is not None and name.endswith(".wkv_b.weight"):
+            n_head = self.hparams["num_attention_heads"]
+            v_head_dim = self.hparams["v_head_dim"]
+            qk_nope_head_dim = self.hparams["qk_nope_head_dim"]
+            if not self.layer_has_rope[bid]:
+                qk_nope_head_dim += self.hparams["qk_rope_head_dim"]
+
+            assert data_torch.shape[0] == n_head * (qk_nope_head_dim + v_head_dim)
+
+            kv_b = data_torch.view(n_head, qk_nope_head_dim + v_head_dim, data_torch.shape[-1])
+            k_b, v_b = torch.split(kv_b, [qk_nope_head_dim, v_head_dim], dim=1)
+            k_b = k_b.transpose(1, 2)
+
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K_B, bid), k_b)
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V_B, bid), v_b)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+class MistralLarge4DSparkModel(MistralLarge4Model):
+    """Mistral Large 4 DSpark draft (mtp_dspark.*) as a DFLASH GGUF, export with --dspark"""
+    model_arch = gguf.MODEL_ARCH.DFLASH
+
+    _DSPARK_ROOT: dict[str, gguf.MODEL_TENSOR] = {
+        "backbone.target_projection.weight": gguf.MODEL_TENSOR.FC,
+        "backbone.target_norm.weight":       gguf.MODEL_TENSOR.ENC_OUTPUT_NORM,
+        "backbone.final_norm.weight":        gguf.MODEL_TENSOR.OUTPUT_NORM,
+        "backbone.mask_embedding":           gguf.MODEL_TENSOR.DSPARK_MASK_EMBD,
+        "autoregressive_head.embedding.weight": gguf.MODEL_TENSOR.DSPARK_MARKOV_W1,
+        "autoregressive_head.output.weight":    gguf.MODEL_TENSOR.DSPARK_MARKOV_W2,
+        "autoregressive_head.joint_proj.weight": gguf.MODEL_TENSOR.DSPARK_JOINT_PROJ,
+        "autoregressive_head.joint_proj.bias":   gguf.MODEL_TENSOR.DSPARK_JOINT_PROJ,
+    }
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        mtp = self.hparams.get("mtp") or {}
+        dspark = mtp.get("dspark")
+        if dspark is None:
+            raise ValueError("Mistral Large 4 DSpark export needs the mtp.dspark config")
+        assert dspark.get("head_type") == "rnn", f"unsupported DSpark head type: {dspark.get('head_type')}"
+        assert dspark.get("draft_input") == "position_specific", f"unsupported DSpark draft input: {dspark.get('draft_input')}"
+
+        windows = {int(v) for v in (mtp.get("ragged_attention") or "").split(",") if v.strip()}
+        assert len(windows) == 1, f"DSpark draft layers must share one sliding window, got {mtp.get('ragged_attention')}"
+
+        self._n_stage = int(mtp["n_layers"])
+        self._depth = int(mtp["depth"])
+        self._window = windows.pop()
+        self._target_layers = [int(v) for v in mtp["target_activation_layers"].split(",")]
+
+        self.layer_has_rope = [True] * self._n_stage
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+        if not name.startswith("mtp_dspark."):
+            return None
+        name = name.removeprefix("mtp_dspark.")
+        if name.startswith("backbone.layers."):
+            return name.removeprefix("backbone."), gen
+        return f"dspark.{name}", gen
+
+    def set_vocab(self):
+        super().set_vocab()
+        # block uses mask_embd, mask token id is not used
+        self.gguf_writer.add_mask_token_id(0)
+
+    def set_gguf_parameters(self):
+        hp = self.hparams
+        w = self.gguf_writer
+        w.add_block_count(self._n_stage)
+        w.add_context_length(hp["max_position_embeddings"])
+        w.add_embedding_length(hp["hidden_size"])
+        w.add_feed_forward_length(hp["intermediate_size"])
+        w.add_head_count(hp["num_attention_heads"])
+        w.add_head_count_kv(1)
+        w.add_key_length(hp["kv_lora_rank"] + hp["qk_rope_head_dim"])
+        w.add_value_length(hp["kv_lora_rank"])
+        w.add_key_length_mla(hp["qk_nope_head_dim"] + hp["qk_rope_head_dim"])
+        w.add_value_length_mla(hp["v_head_dim"])
+        w.add_kv_lora_rank(hp["kv_lora_rank"])
+        w.add_rope_dimension_count(hp["qk_rope_head_dim"])
+        w.add_rope_freq_base(float(hp["rope_theta"]))
+        w.add_layer_norm_rms_eps(hp["rms_norm_eps"])
+        MistralModel.set_mistral_config(w, hp)
+        w.add_sliding_window(self._window)
+        w.add_final_logit_softcapping(hp["softmax_tanh_gating"])
+        w.add_block_size(self._depth)
+        # draft reads layer output = input of next layer
+        w.add_target_layers([layer + 1 for layer in self._target_layers])
+        w.add_sample_from_anchor(True)
+        w.add_has_confidence_head(False)
+        w.add_file_type(self.ftype)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("dspark."):
+            key = name.removeprefix("dspark.")
+            if key not in self._DSPARK_ROOT:
+                raise ValueError(f"Unexpected DSpark tensor {name!r}")
+            suffix = ".bias" if key.endswith(".bias") else ".weight"
+            yield (self.format_tensor_name(self._DSPARK_ROOT[key], None, suffix), data_torch)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
@@ -428,11 +428,19 @@ class NemotronHModel(GraniteHybridModel):
    is_moe: bool = False
    supports_mtp_export = True
    _experts: list[dict[str, Tensor]] | None = None
+    # tokens past the embedding rows (left out of the vocab), and special tokens that are printed as text
+    _vocab_skip: tuple[str, ...] = ()
+    _user_defined_tokens: re.Pattern[str] | None = None

    _SSM_LAYER_TYPES = {"mamba", "linear_attention"}
    _ATTN_LAYER_TYPES = {"attention", "full_attention"}
    _MLP_LAYER_TYPES = {"moe"}

+    def _map_fp8_weight_names(self, name: str) -> tuple[str, ...]:
+        if ".mixer.experts." in name:
+            name = name.replace("backbone.", "model.").replace(".mixer.experts.", ".mlp.experts.")
+        return super()._map_fp8_weight_names(name)
+
    def __init__(self, *args, **kwargs):
        # We have to determine the correct model architecture (MoE vs non-MoE) before
        # calling the parent __init__. This is because the parent constructor
@@ -611,7 +619,11 @@ class NemotronHModel(GraniteHybridModel):
        vocab_size = -(vocab_size // -pad_vocab) * pad_vocab
        self.hparams["vocab_size"] = vocab_size

-        assert max(tokenizer.vocab.values()) < vocab_size  # ty: ignore[unresolved-attribute]
+        # draft-model converters (DFlash, LiLiCorr) call this with their own instance, which has neither hook
+        vocab_skip = getattr(self, "_vocab_skip", ())
+        user_defined_tokens = getattr(self, "_user_defined_tokens", None)
+
+        assert max(id_ for tok, id_ in tokenizer.vocab.items() if tok not in vocab_skip) < vocab_size  # ty: ignore[unresolved-attribute]

        tokpre = self.get_vocab_base_pre(tokenizer)

@@ -633,7 +645,8 @@ class NemotronHModel(GraniteHybridModel):
                        if previous_token != token:
                            logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")

-                    if added_tokens_decoder[i].special or self.does_token_look_special(token):
+                    printed = user_defined_tokens is not None and user_defined_tokens.fullmatch(token)
+                    if (added_tokens_decoder[i].special or self.does_token_look_special(token)) and not printed:
                        toktypes.append(gguf.TokenType.CONTROL)
                    else:
                        token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ")  # pre-normalize user-defined spaces
@@ -829,3 +842,140 @@ class NemotronHPuzzleModel(NemotronHModel):
        if item[0].startswith("mtp."):
            return None
        return super().filter_tensors(item)
+
+
+# NeMo speechlm2 SALM checkpoint: a Nemotron-H decoder under llm.*, the speech encoder under perception.*
+@ModelBase.register("Nemotron3_5TranscribeForConditionalGeneration")
+@ModelBase.example("nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B")
+class NemotronTranscribeModel(NemotronHModel):
+    model_arch = gguf.MODEL_ARCH.NEMOTRON_H
+    # the audio placeholder (id vocab_size) has no embedding row; speaker, event and style tags print with the transcript
+    _vocab_skip = ("<|audio|>",)
+    _user_defined_tokens = re.compile(r"<spk:\d+>|<ev:[a-z_]+>|<sty:(?:stt|end):[a-z_]+>")
+    _renames = {
+        "llm.model.embed_tokens.weight": "backbone.embeddings.weight",
+        "llm.model.norm.weight":         "backbone.norm_f.weight",
+        "llm.lm_head.weight":            "lm_head.weight",
+    }
+    # grouped experts: [n_expert, n_in, n_out] -> [n_expert, n_out, n_in]
+    _grouped_experts = {"gate_and_up_projs": "up_proj", "down_projs": "down_proj"}
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+        if name.startswith(("perception.", "ctc_timestamp.")) or "_extra_state" in name:
+            return None
+        if not name.startswith("llm."):
+            # the FP8 and NVFP4 releases store the decoder as backbone.*
+            raise ValueError(f"unexpected tensor {name}: only the BF16 checkpoint layout (llm.*) is supported")
+        if name in cls._renames:
+            name = cls._renames[name]
+        elif name.startswith("llm.model.layers."):
+            name = "backbone.layers." + name[len("llm.model.layers."):]
+        elif name.startswith("llm.mtp.layers."):
+            name = "mtp.layers." + name[len("llm.mtp.layers."):]
+        else:
+            raise ValueError(f"unexpected decoder tensor {name}")
+        return super().filter_tensors((name.replace("._fp32_params.", "."), gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        leaf = name.rsplit(".", 1)[-1]
+        if ".mixer.experts." in name and leaf in self._grouped_experts:
+            if name.startswith("mtp."):
+                assert self._mtp_bid is not None, f"{name}: MTP head is not exported"
+                bid = self._mtp_bid
+            assert bid is not None
+            data_torch = data_torch.permute(0, 2, 1).contiguous()
+            name = f"model.layers.{bid}.mlp.experts.{self._grouped_experts[leaf]}.weight"
+            yield from ModelBase.modify_tensors(self, data_torch, name, bid)
+            return
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Nemotron3_5TranscribeForConditionalGeneration")
+class NemotronTranscribeAudioModel(MmprojModel):
+    # NeMo ParallelExpertEncoder: ASR and speaker transformers, speaker activity fused into the ASR states
+    has_vision_encoder = False
+    has_audio_encoder = True
+
+    def get_audio_config(self) -> dict[str, Any] | None:
+        asr = self.global_config["pe_encoder_config"]["asr_encoder_cfg"]
+        return {
+            "hidden_size":         asr["d_model"],
+            "intermediate_size":   int(float(asr.get("ff_expansion", 4)) * asr["d_model"]),
+            "num_hidden_layers":   asr["n_layers"],
+            "num_attention_heads": asr["n_heads"],
+        }
+
+    @staticmethod
+    def _rope_dims(cfg: dict[str, Any]) -> int:
+        assert cfg.get("self_attention_model") == "rope" and cfg.get("subsampling", "feature_stacking") == "feature_stacking"
+        assert cfg.get("attn_mode", "full") == "full" and not cfg.get("xscaling", False) and cfg.get("pre_block_norm", True)
+        assert float(cfg.get("rope_base", 10000.0)) == 10000.0
+        n_rot = int(cfg["d_model"] // cfg["n_heads"] * float(cfg.get("rotary_fraction", 1.0)))
+        assert n_rot >= 2 and n_rot % 2 == 0
+        return n_rot
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        pe = self.global_config["pe_encoder_config"]
+        asr = pe["asr_encoder_cfg"]
+        diar = pe["diarization_model_cfg"]
+        spk = diar["encoder"]
+        assert pe.get("asr_encoder_type") == "transformer" and pe.get("speaker_feature_mode", "thresholded") == "thresholded"
+        assert pe.get("asr_normalize_type") == "per_feature" and pe.get("diar_normalize_type") in (None, "NA")
+        assert asr["subsampling_factor"] == spk["subsampling_factor"] == 8 and asr["feat_in"] == spk["feat_in"]
+        assert self._rope_dims(spk) == spk["d_model"] // spk["n_heads"] and not spk.get("qkv_bias", False)
+        assert diar.get("high_resolution", False)
+        # constants in clip: the ParallelExpertEncoder online settings and the Sortformer streaming config
+        assert (pe.get("online_inference_length", 500), pe.get("chunk_left_context", 50), pe.get("chunk_right_context", 50),
+                pe.get("diar_spkcache_len", 188), pe.get("diar_fifo_len", 40), pe.get("diar_spkcache_update_period", 300),
+                pe.get("speaker_activity_threshold", 0.5)) == (500, 50, 50, 188, 40, 300, 0.5), \
+            "streaming settings differ from the constants in tools/mtmd/models/nemotron-transcribe.cpp"
+        sm = diar["sortformer_modules"]
+        assert (sm["spkcache_sil_frames_per_spk"], sm["pred_score_threshold"], sm["scores_boost_latest"], sm["strong_boost_rate"],
+                sm["weak_boost_rate"], sm["min_pos_scores_rate"], sm["use_learnable_sil_emb"]) == (1, 0.25, 0.05, 0.75, 1.5, 0.5, True), \
+            "speaker cache settings differ from the constants in tools/mtmd/models/nemotron-transcribe.cpp"
+
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.NEMOTRON_TRANSCRIBE)
+        self.gguf_writer.add_audio_num_mel_bins(asr["feat_in"])
+        self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
+        self.gguf_writer.add_audio_subsampling_factor(asr["subsampling_factor"])
+        self.gguf_writer.add_audio_rope_dims(self._rope_dims(asr))
+        self.gguf_writer.add_audio_spk_block_count(spk["n_layers"])
+        self.gguf_writer.add_audio_spk_head_count(spk["n_heads"])
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        # ParallelExpertEncoder._build_sinusoid_position_encoding
+        pe = self.global_config["pe_encoder_config"]
+        n_spk = int(pe["diarization_model_cfg"]["sortformer_modules"]["num_spks"])
+        d = int(pe["asr_encoder_cfg"]["d_model"])
+        scale = float(pe.get("spk_kernel_scale", 1.0))
+        pos = torch.arange(n_spk, dtype=torch.float32)[:, None]
+        div = torch.exp(torch.arange(0, d, 2, dtype=torch.float32) * -(np.log(10000.0) / d))
+        kernel = torch.zeros(n_spk, d, dtype=torch.float32)
+        kernel[:, 0::2] = torch.sin(pos * div)
+        kernel[:, 1::2] = torch.cos(pos * div)
+        # stored as [d, n_spk]: the graph multiplies it with the speaker activity directly
+        yield self.format_tensor_name(gguf.MODEL_TENSOR.A_FUSION_SPK_KERNEL, suffix=""), (kernel * scale).T.contiguous()
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.A_SPK_HEAD_UPSAMPLE, None):
+            return gguf.GGMLQuantizationType.F16  # ggml_conv_1d kernel
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+        if not name.startswith("perception.") or "_extra_state" in name:
+            return None
+        # training-only head; the speaker model's unused front end
+        if name.startswith(("perception.encoder.diarization_model.sortformer_modules.hidden_to_spks.",
+                            "perception.encoder.diarization_model.preprocessor.")):
+            return None
+        return name, gen
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name == "perception.preprocessor.featurizer.fb":
+            data_torch = data_torch.squeeze(0)
+        yield from super().modify_tensors(data_torch, name, bid)
@@ -5,6 +5,7 @@ from pathlib import Path
 from typing import Any, Iterable, TYPE_CHECKING

 import torch
+import torch.nn.functional as F

 if TYPE_CHECKING:
    from torch import Tensor
@@ -45,6 +46,102 @@ def _tensor_shapes(dir_model: Path) -> dict[str, tuple[int, ...]]:
        return {name: tuple(part[name].shape) for name in part.keys()}


+def _has_voice_encoder(dir_model: Path) -> bool:
+    # without voice cloning, the mimi encoder weights are all zero
+    part_names = ModelBase.get_model_part_names(dir_model, "model", ".safetensors")
+    with gguf.utility.SafetensorsLocal(dir_model / part_names[0]) as part:
+        nonzero = {bool(part[name].mmap_bytes().any()) for name in part.keys() if name.startswith("mimi.encoder")}
+    if len(nonzero) != 1:
+        raise ValueError(f"{dir_model}: the mimi encoder is partly zeroed out")
+    return nonzero.pop()
+
+
+def _load_safetensors(path: Path, prefixes: tuple[str, ...]) -> dict[str, Tensor]:
+    dtypes = {"F32": torch.float32, "BF16": torch.bfloat16, "I64": torch.int64}
+    with gguf.utility.SafetensorsLocal(path) as part:
+        return {name: torch.from_numpy(part[name].mmap_bytes()).view(dtypes[part[name].dtype]).reshape(part[name].shape)
+                for name in part.keys() if name.startswith(prefixes)}
+
+
+def _rope_pairs(x: Tensor, n_head: int, rope_theta: float) -> Tensor:
+    # ggml NORM mode rope, x: [n_pos, n_embd]
+    n_pos, n_embd = x.shape
+    head_dim = n_embd // n_head
+    angle = torch.arange(n_pos, dtype=x.dtype)[:, None, None] * \
+        torch.pow(rope_theta, -torch.arange(0, head_dim, 2, dtype=x.dtype) / head_dim)
+    x = x.view(n_pos, n_head, head_dim // 2, 2)
+    return torch.stack([x[..., 0] * angle.cos() - x[..., 1] * angle.sin(),
+                        x[..., 0] * angle.sin() + x[..., 1] * angle.cos()], -1).reshape(n_pos, n_embd)
+
+
+def _voice_prompt_embd(weights: dict[str, Tensor], kv: list[Tensor], n_head: int, rope_theta: float) -> tuple[Tensor, float]:
+    # the voice state only has K/V of each layer (kv[il] is [k/v, pos, n_embd]): layer 0 V gives each frame up to its scale, layer 1 V gives the scale
+    # returns the frames without bos_before_voice, and the max relative K/V error when the frames are prompted again
+    w = {name: t.double() for name, t in weights.items()}
+    kv = [t.double() for t in kv]
+    k0, v0, v1 = kv[0][0], kv[0][1], kv[1][1]
+    n_pos, n_embd = v0.shape
+    head_dim = n_embd // n_head
+
+    def p(il: int, name: str) -> Tensor:
+        return w[f"flow_lm.transformer.layers.{il}.{name}"]
+
+    def norm(x: Tensor, il: int, name: str) -> Tensor:
+        return F.layer_norm(x, (n_embd,), p(il, f"{name}.weight"), p(il, f"{name}.bias"), 1e-5)
+
+    def heads(t: Tensor) -> Tensor:
+        return t.reshape(n_pos, n_head, head_dim).transpose(0, 1)
+
+    def rows(t: Tensor) -> Tensor:
+        return t.transpose(0, 1).reshape(n_pos, n_embd)
+
+    def attn(il: int, h: Tensor, k: Tensor, v: Tensor) -> Tensor:
+        q = _rope_pairs(h @ p(il, "self_attn.in_proj.weight").chunk(3)[0].T, n_head, rope_theta)
+        return rows(F.scaled_dot_product_attention(heads(q), heads(k), heads(v), is_causal=True)) @ p(il, "self_attn.out_proj.weight").T
+
+    def ffn(il: int, x: Tensor) -> Tensor:
+        return F.gelu(norm(x, il, "norm2") @ p(il, "linear1.weight").T) @ p(il, "linear2.weight").T
+
+    wv = p(0, "self_attn.in_proj.weight").chunk(3)[2]
+    h = torch.linalg.solve(wv, v0.T).T                   # norm1(x)
+    z = (h - p(0, "norm1.bias")) / p(0, "norm1.weight")  # (x - mean(x)) / std(x)
+
+    # fit z on [speaker_proj, 1] to get the latent direction back
+    proj = w["flow_lm.speaker_proj_weight"]
+    fit = torch.linalg.lstsq(torch.cat([proj, torch.ones(n_embd, 1, dtype=proj.dtype)], 1), z[1:].T).solution
+    frames = (proj @ fit[:-1]).T
+
+    a0 = attn(0, h, k0, v0)[1:]
+    wv1 = p(1, "self_attn.in_proj.weight").chunk(3)[2]
+
+    def err(scale: Tensor) -> Tensor:  # per frame relative error of layer 1 V
+        x = scale[:, None] * frames + a0
+        x = x + ffn(0, x)
+        return (norm(x, 1, "norm1") @ wv1.T - v1[1:]).norm(dim=1) / v1[1:].norm(dim=1)
+
+    grid = torch.logspace(-3, 3, 49, dtype=torch.float64)
+    best = torch.stack([err(s.expand(n_pos - 1)) for s in grid]).argmin(0)
+    lo, hi = grid[(best - 1).clamp(min=0)], grid[(best + 1).clamp(max=len(grid) - 1)]
+    for _ in range(40):  # golden section search, all frames at once
+        m1, m2 = hi - 0.618 * (hi - lo), lo + 0.618 * (hi - lo)
+        left = err(m1) < err(m2)
+        lo, hi = torch.where(left, lo, m1), torch.where(left, m2, hi)
+    frames = (lo + hi)[:, None] / 2 * frames
+
+    # prompt the frames again and compare K/V with the shipped state
+    x = torch.cat([w["flow_lm.bos_before_voice"].reshape(1, n_embd), frames])
+    drift = 0.0
+    for il, (k_ref, v_ref) in enumerate(kv):
+        h = norm(x, il, "norm1")
+        _, wk, wv = p(il, "self_attn.in_proj.weight").chunk(3)
+        k, v = _rope_pairs(h @ wk.T, n_head, rope_theta), h @ wv.T
+        for a, b in ((k, k_ref), (v, v_ref)):
+            drift = max(drift, ((a - b).norm(dim=1) / b.norm(dim=1)).median().item())
+        x = x + attn(il, h, k, v)
+        x = x + ffn(il, x)
+    return frames, drift
+
+
@ModelBase.register_hparams_loader(lambda dir_model: "flow_lm.bos_emb" in _tensor_shapes(dir_model))
 def _load_hparams(dir_model: Path) -> dict[str, Any]:
    logger.info("gguf: detected pocket-tts checkpoint, deriving hparams from tensor shapes")
@@ -194,22 +291,27 @@ class PocketTTSMmprojModel(MmprojModel):
        (gguf.MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_Q, gguf.MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_K, gguf.MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_V),
    )

+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.has_audio_encoder = _has_voice_encoder(self.dir_model)
+
    def set_gguf_parameters(self):
        self.gguf_writer.add_file_type(self.ftype)
        assert self.hparams_audio is not None

        # voice-prompt encoder: mimi encoder + speaker_proj
-        self.gguf_writer.add_clip_has_audio_encoder(True)
-        # note: the 24kHz sample rate is hardcoded on the clip.cpp side, like the other audio models
-        self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.POCKETTTS_SPKENC)
-        self.gguf_writer.add_audio_projection_dim(self.n_embd_text)
-        self.gguf_writer.add_audio_block_count(self.hparams_audio["num_hidden_layers"])
-        self.gguf_writer.add_audio_embedding_length(self.hparams_audio["hidden_size"])
-        self.gguf_writer.add_audio_feed_forward_length(self.hparams_audio["intermediate_size"])
-        self.gguf_writer.add_audio_head_count(self.hparams_audio["num_attention_heads"])
-        self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
-        # mimi convolves the waveform directly, it is passed around as a 1-row "mel"
-        self.gguf_writer.add_audio_num_mel_bins(1)
+        if self.has_audio_encoder:
+            self.gguf_writer.add_clip_has_audio_encoder(True)
+            # note: the 24kHz sample rate is hardcoded on the clip.cpp side, like the other audio models
+            self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.POCKETTTS_SPKENC)
+            self.gguf_writer.add_audio_projection_dim(self.n_embd_text)
+            self.gguf_writer.add_audio_block_count(self.hparams_audio["num_hidden_layers"])
+            self.gguf_writer.add_audio_embedding_length(self.hparams_audio["hidden_size"])
+            self.gguf_writer.add_audio_feed_forward_length(self.hparams_audio["intermediate_size"])
+            self.gguf_writer.add_audio_head_count(self.hparams_audio["num_attention_heads"])
+            self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
+            # mimi convolves the waveform directly, it is passed around as a 1-row "mel"
+            self.gguf_writer.add_audio_num_mel_bins(1)

        # generation: flow-matching decoder + mimi decoder
        # the SEANet and flow net hparams are constant across the family, clip.cpp holds them
@@ -229,12 +331,57 @@ class PocketTTSMmprojModel(MmprojModel):
        # conv1d/conv1d_dw kernels must be F16, ggml_conv_1d(_dw) has no BF16 path
        if ".seanet." in new_name or new_name in ("a.downsample.conv.weight", "a.gen.wav.upsample.weight"):
            return gguf.GGMLQuantizationType.F16
+        # voices are read on the host
+        if new_name.startswith("voice."):
+            return gguf.GGMLQuantizationType.F32
        return False

+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        # embeddings/<name>.safetensors hold the K/V state after the voice prompt (see get_state_for_audio_prompt() in the reference), turn it back into prompt embeddings
+        paths = sorted((self.dir_model / "embeddings").glob("*.safetensors"))
+        if not paths:
+            return
+        weights = _load_safetensors(self.dir_model / "model.safetensors",
+                                    ("flow_lm.transformer.layers.", "flow_lm.speaker_proj_weight", "flow_lm.bos_before_voice"))
+        n_layer = self.global_config["num_hidden_layers"]
+        for path in paths:
+            state = _load_safetensors(path, ("transformer.layers.",))
+            kv = []
+            for il in range(n_layer):
+                prefix = f"transformer.layers.{il}.self_attn/"
+                if any(prefix + k not in state for k in ("cache", "offset")):
+                    break
+                n_pos = int(state[prefix + "offset"].item())
+                # older packs have no pad entry
+                if prefix + "pad" in state and int(state[prefix + "pad"].item()) != 0:
+                    break
+                kv.append(state[prefix + "cache"][:, 0, :n_pos].flatten(2))  # [k/v, pos, n_embd]
+            if len(kv) != n_layer or any(t.shape != kv[0].shape for t in kv) or kv[0].shape[1] < 2 or \
+                    kv[0].shape[2] != self.global_config["hidden_size"]:
+                logger.warning(f"voice {path.stem}: skipped, not an unpadded voice state of this backbone")
+                continue
+            embd, drift = _voice_prompt_embd(weights, kv, self.global_config["num_attention_heads"],
+                                             self.global_config["rope_theta"])
+            # some packs ship a state made with other weights, the reference still uses it: keep up to 50% error, warn above 5%
+            if drift > 0.5:
+                logger.warning(f"voice {path.stem}: skipped, its state does not belong to these weights (error {drift:.1e})")
+                continue
+            log = logger.warning if drift > 0.05 else logger.info
+            log(f"voice {path.stem}: {embd.shape[0]} frames, prompting them again gives the shipped state within {drift:.1e}")
+            yield (f"voice.{path.stem}", embd.float())
+
    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        del bid  # the block index of the mimi transformers is parsed here, not by the base class
        T = gguf.MODEL_TENSOR

+        if name.startswith("voice."):
+            yield (name, data_torch)
+            return
+
+        if not self.has_audio_encoder and (name.startswith(("mimi.encoder", "mimi.downsample."))
+                                           or name == "flow_lm.speaker_proj_weight"):
+            return  # voice-prompt encoder
+
        if name in ("flow_lm.bos_emb", "flow_lm.bos_before_voice", "flow_lm.conditioner.embed.weight"):
            return  # folded into the backbone embedding table
        if name.startswith("flow_lm.transformer.") or name.startswith("flow_lm.out_norm."):
@@ -83,6 +83,15 @@ class Qwen2Model(TextModel):
 class Qwen2MoeModel(TextModel):
    model_arch = gguf.MODEL_ARCH.QWEN2MOE

+    def _map_fp8_weight_names(self, name: str) -> tuple[str, ...]:
+        if name.removesuffix(".weight").endswith(".mlp.experts.gate_up_proj"):
+            bid = next(int(part) for part in self.map_tensor_name(name).split(".") if part.isdecimal())
+            return (
+                self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid),
+                self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_EXP, bid),
+            )
+        return super()._map_fp8_weight_names(name)
+
    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
@@ -382,6 +391,16 @@ class _QwenMtpMixin:
 class Qwen3NextModel(_QwenMtpMixin, Qwen2MoeModel):
    model_arch = gguf.MODEL_ARCH.QWEN3NEXT

+    def _map_fp8_weight_names(self, name: str) -> tuple[str, ...]:
+        if name.endswith(".linear_attn.in_proj_qkvz.weight"):
+            bid = next(int(part) for part in self.map_tensor_name(name).split(".") if part.isdecimal())
+            # Both split projections keep the original per-tensor scales.
+            return (
+                self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_QKV, bid),
+                self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_GATE, bid),
+            )
+        return super()._map_fp8_weight_names(name)
+
    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_ssm_conv_kernel(self.hparams["linear_conv_kernel_dim"])
@@ -58,7 +58,7 @@ def parse_args() -> argparse.Namespace:
    )
    parser.add_argument(
        "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "tq1_0", "tq2_0", "auto"], default="auto",
-        help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, tq1_0 or tq2_0 for ternary, and auto for the highest-fidelity 16-bit float type",
+        help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, tq1_0 or tq2_0 for ternary, and auto to preserve supported FP8 weights and otherwise select the highest-fidelity 16-bit float type",
    )
    parser.add_argument(
        "--bigendian", action="store_true",
@@ -127,7 +127,7 @@ def parse_args() -> argparse.Namespace:
    )
    parser.add_argument(
        "--dspark", action="store_true",
-        help="Export only the DeepSeek-V4 DSpark draft tensors as a separate GGUF.",
+        help="Export only the DeepSeek-V4 or Mistral Large 4 DSpark draft tensors as a separate GGUF.",
    )
    parser.add_argument(
        "--mistral-format", action="store_true",
@@ -255,6 +255,9 @@ def main() -> None:
            assert hparams.get("vision_encoder") is not None, "This model does not support multimodal"
            from conversion.pixtral import PixtralModel
            model_class = PixtralModel
+        elif hparams.get("moe") is not None and hparams.get("dsa") is not None:
+            from conversion.mistral4 import MistralLarge4Model
+            model_class = MistralLarge4Model
        elif hparams.get("moe") is not None:
            from conversion.mistral import MistralMoeModel
            model_class = MistralMoeModel
@@ -267,10 +270,16 @@ def main() -> None:
            sys.exit(1)

        if args.dspark:
-            if is_mistral_format or model_architecture not in ("DeepseekV4ForCausalLM", "DeepseekV41ForCausalLM"):
-                logger.error("--dspark is only supported for DeepseekV4ForCausalLM and DeepseekV41ForCausalLM")
+            if is_mistral_format or model_architecture == "MistralLarge4ForConditionalGeneration":
+                if model_class.__name__ != "MistralLarge4Model":
+                    logger.error("--dspark is only supported for Mistral Large 4 among Mistral-format models")
+                    sys.exit(1)
+                from conversion.mistral4 import MistralLarge4DSparkModel
+                model_class = MistralLarge4DSparkModel
+            elif model_architecture not in ("DeepseekV4ForCausalLM", "DeepseekV41ForCausalLM"):
+                logger.error("--dspark is only supported for DeepseekV4ForCausalLM, DeepseekV41ForCausalLM and Mistral Large 4")
                sys.exit(1)
-            if model_architecture == "DeepseekV41ForCausalLM":
+            elif model_architecture == "DeepseekV41ForCausalLM":
                from conversion.deepseek41 import DeepseekV41DSparkModel
                model_class = DeepseekV41DSparkModel
            else:
@@ -198,6 +198,7 @@ pre_computed_hashes = [
    # jina-v2-de variants
    {"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/aari1995/German_Semantic_V3", "chkhsh": "b3d1dd861f1d4c5c0d2569ce36baf3f90fe8a102db3de50dd71ff860d91be3df"},
    {"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/evilfreelancer/ruGPT3XL", "chkhsh": "0fe1cf6eda062318a1af7270f3331a85c539a01778ff948e24388e949c5282f4"},
+    {"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Lernex/Metis-1.6-Logos", "chkhsh": "df1bc34a6366466bcb36a9a1b1cc0afdb08cfe21c9250602c930f159085390a9"},
    # lfm2 variants
    {"name": "lfm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LiquidAI/LFM2.5-8B-A1B", "chkhsh": "9e454714343b69b99b71795c1d27a68c2a1d15dab111f4d353109f966af29da7"},
    # hrm-text (DFM Mimir) is SPM-style BPE: normalizer maps ' ' -> '▁', merges
@@ -161,7 +161,7 @@ lspci -n | grep -Eo '19e5:d[0-9a-f]{3}' | cut -d: -f2
 | FP16                   | Support | Support |
 | Q8_0                   | Support | Partial |
 | Q4_0                   | Support | Partial |
-| BF16                   | Support |         |
+| BF16                   | Support |    No   |

 > **310P note**
 > - `Q8_0`: data transform / buffer path is implemented, and `GET_ROWS` is supported, but quantized `MUL_MAT` / `MUL_MAT_ID` are not supported.
@@ -274,7 +274,9 @@ cmake --build build --config release

    **Notes**:

-      - CANN backend only supports FP16/Q4_0/Q8_0 models currently.
+      - On 910B the CANN backend supports FP16, BF16 and Q4_0/Q8_0 models. On 310P, BF16 is not
+        supported and quantized `MUL_MAT` / `MUL_MAT_ID` are not implemented, so only FP16 models
+        are supported (see [DataType Supports](#datatype-supports)).

 2. **Launch inference**

@@ -355,3 +357,7 @@ Enable ACL graph execution during the prefill stage, default is false. This opti
 ### GGML_CANN_OPERATOR_FUSION

 Enable operator fusion during computation, default is false. This option fuses compatible operators (e.g., ADD + RMS_NORM) to reduce overhead and improve performance.
+
+### GGML_CANN_NO_PINNED
+
+Disable pinned (host-locked) host buffers. When it is set (any value), `aclrtMallocHost` is not called: the CANN host buffer type allocates regular CPU memory instead and the backend reports no `host_buffer` capability, so host-side staging uses pageable memory. Use it when pinned host allocation fails, or when the pinned memory is needed by other processes on the host.
@@ -784,6 +784,7 @@ User can use the device management in [docs/multi-gpu.md](https://github.com/ggm
 | GGML_SYCL_TARGET   | INTEL *(default)*                     | Set the SYCL target device type.            |
 | GGML_SYCL_DEVICE_ARCH | Optional                           | Set the SYCL device architecture. Setting the device architecture can improve the performance. See the table [--offload-arch](https://github.com/intel/llvm/blob/sycl/sycl/doc/design/OffloadDesign.md#--offload-arch) for a list of valid architectures. |
 | GGML_SYCL_F16      | OFF *(default)* \|ON *(optional)*     | Enable FP16 build with SYCL code path. [1.](#sycl-f16-footnote) |
+| GGML_SYCL_SUPPORT_XMX | ON *(default)* \|OFF *(Optional)* | Build the code paths that use XMX (DPAS) instructions. Set OFF for AOT builds targeting iGPUs without XMX, which otherwise fail to compile. |
 | GGML_SYCL_GRAPH    | ON *(default)* \|OFF *(Optional)*     | Enable build with [SYCL Graph extension](https://github.com/intel/llvm/blob/sycl/sycl/doc/extensions/experimental/sycl_ext_oneapi_graph.asciidoc). |
 | GGML_SYCL_DNN      | ON *(default)* \|OFF *(Optional)*     | Enable build with oneDNN.                   |
 | GGML_SYCL_HOST_MEM_FALLBACK | ON *(default)* \|OFF *(Optional)* | Allow host memory fallback when device memory is full during quantized weight reorder. Enables inference to continue at reduced speed (reading over PCIe) instead of failing. Requires Linux kernel 6.8+. |
@@ -812,6 +813,7 @@ User can use the device management in [docs/multi-gpu.md](https://github.com/ggm
 | GGML_SYCL_ENABLE_DNN | 0 or 1 (default)| Enable running computations through oneDNN and always use oneMKL. |
 | GGML_SYCL_FA_ONEDNN | 1 (default) or 0 | Enable the oneDNN fused SDPA (flash-attention) path on supported GPUs. Set to 0 to always use the native SYCL flash-attention kernel. |
 | GGML_SYCL_FA_ONEDNN_MAX_KV | 0 (default, disabled) or positive integer | By default (0), all sequences are handled by the oneDNN fused SDPA path, regardless of KV length; a positive value caps that length, past which sequences fall back to the native kernel. If GPU driver watchdog resets (DEVICE_LOST) occur during long-context inference, set this near the context depth where they start, e.g. 24576. |
+| GGML_SYCL_FA_DPAS | 1 (default) or 0 | Enable the XMX flash-attention kernel for a q4_0 or q8_0 KV cache (`-ctk q4_0 -ctv q4_0` or `-ctk q8_0 -ctv q8_0`) on Xe2 or newer GPUs (Arc B-series, Lunar Lake, Panther Lake, Wildcat Lake). It reads the quantized cache directly for decode and batches of up to 31 tokens, head sizes 64, 96, 128, 256 and 512, up to 32 query heads per KV head. Set to 0 to use the native SYCL flash-attention kernels. |
 | GGML_SYCL_ENABLE_VMM | 0 or 1 (default) | Enable the virtual-memory device pool. |
 | GGML_SYCL_ENABLE_MKL_FA | 1 (default) or 0 | Enable oneMKL GEMM flash attention for XMX-accelerated prompt processing with quantized KV cache. Automatically activates during prefill (prompt processing) when all conditions are met: (1) flash-attn enabled (`-fa` or `--flash-attn on`), (2) KV cache quantized (`--cache-type-k q8_0 --cache-type-v q8_0` or other `*_0/*_1` types), (3) batch size ≥ 1024 (`--batch-size 1024`), (4) prompt length ≥ 1024 tokens. Set to 0 to force the TILE kernel for A/B testing. Example minimum command: `llama-cli -m model.gguf -fa -ngl 99 --cache-type-k q8_0 --cache-type-v q8_0 --batch-size 1024 -p "your prompt"` |
 | GGML_SYCL_MKL_FA_DEBUG | 0 (default) or 1 | Enable per-call diagnostic logging for MKL flash attention: GEMM/softmax timings, interleaved-head detection, and buffer memory usage. |
@@ -846,6 +848,18 @@ User can use the device management in [docs/multi-gpu.md](https://github.com/ggm
   Then benchmark with your preferred tool, such as `llama-bench`.
   For graph activity details (including reuse), set `GGML_SYCL_DEBUG=1` and look for `[SYCL-GRAPH]` messages.

+### Flash-attention kernels
+
+Attention dispatches on shape and hardware.
+
+DPAS is the quantized path. q4_0 or q8_0 KV cache, decode and batches up to 31 tokens, Xe2 and newer only. It issues dpas straight on the quantized blocks and never materialises an f16 copy. vISA gets used here because joint_matrix has no s4 type.
+
+oneDNN does XMX through SDPA but converts KV to f16 first, so it ends up on prefill: 32+ tokens and 1K+ keys. MKL is the prompt processing equivalent through oneMKL GEMM, also f16.
+
+VEC/TILE are plain kernels with no XMX in them. They run when DPAS is off or the hardware doesn't support it.
+
+joint_matrix belongs to the matmul GEMMs. Attention doesn't use it.
+
 ## Compile-time Flags

 Pass these via `CXXFLAGS` or add a one-off `#define` to enable a flag on the spot.
@@ -249,7 +249,7 @@ IBM VXE/VXE2 SIMD acceleration depends on the BLAS implementation. It is strongl
 | MXFP4      | ✅           | ❓    | ❓     |
 | Q5_0       | ✅           | ❓    | ❓     |
 | Q5_1       | ✅           | ❓    | ❓     |
-| Q8_0       | ✅           | ❓    | ❓     |
+| Q8_0       | ✅           | ✅    | ❓     |
 | Q2_K       | 🚫           | ❓    | ❓     |
 | Q3_K       | ✅           | ❓    | ❓     |
 | Q4_K       | ✅           | ❓    | ❓     |
@@ -266,11 +266,11 @@ IBM VXE/VXE2 SIMD acceleration depends on the BLAS implementation. It is strongl
 | IQ1_M      | 🚫           | ❓    | ❓     |
 | IQ4_NL     | ✅           | ❓    | ❓     |
 | IQ4_XS     | ✅           | ❓    | ❓     |
-| FP32->FP16 | 🚫           | ❓    | ❓     |
+| FP32->FP16 | ✅           | ❓    | ❓     |
 | FP16->FP32 | 🚫           | ❓    | ❓     |

 -   ✅ - acceleration available
 -   🚫 - acceleration unavailable, will still run using scalar implementation
 -   ❓ - acceleration unknown, please contribute if you can test it yourself

-Last Updated by **Aaron Teo (aaron.teo1@ibm.com)** on Sep 8, 2026.
+Last Updated by **Aaron Teo (aaron.teo1@ibm.com)** on Nov 3, 2026.
@@ -481,7 +481,7 @@ cmake --build build --config Release
 Now you can load the model in conversation mode using `Vulkan`

 ```sh
-build/bin/Release/llama-cli -m "[PATH TO MODEL]" -ngl 100 -c 16384 -t 10 -n -2 -cnv
+build/bin/Release/llama-cli -m "[PATH TO MODEL]" -ngl 100 -c 16384 -t 10 -n -2
 ```

 **MSYS2**
@@ -14,22 +14,22 @@ Legend:

 | Operation | BLAS | CANN | CPU | CUDA | ET | HTP | MTL | OpenCL | SYCL | Vulkan | WebGPU | ZenDNN | zDNN |
 |-----------|------|------|------|------|------|------|------|------|------|------|------|------|------|
-|                              ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              ACC | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             ADD1 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                           ARANGE | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                           ARANGE | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             CEIL | ❌ | ❌ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             CEIL | ❌ | ❌ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        COL2IM_1D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
-|                       CONV_1D_DW | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ |
+|                       CONV_1D_DW | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
 |                          CONV_2D | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          CONV_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
@@ -38,49 +38,49 @@ Legend:
 |                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |               CROSS_ENTROPY_LOSS | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |          CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                           CUMSUM | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             DIAG | ❌ | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                           CUMSUM | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             DIAG | ❌ | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              DIV | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              DIV | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                     DSV4_HC_COMB | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                     DSV4_HC_POST | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                      DSV4_HC_PRE | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                              DUP | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                              EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                            EXPM1 | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             FILL | ❌ | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                            EXPM1 | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             FILL | ❌ | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
-|                            FLOOR | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                  GATED_DELTA_NET | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | 🟡 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                            FLOOR | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                  GATED_DELTA_NET | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                GATED_LINEAR_ATTN | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                        GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                      GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                        GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                      GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                       GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
 |                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
 |                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                           IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                        IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | 🟡 | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                          L2_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | 🟡 | ❌ | ❌ |
+|                        IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | 🟡 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                          L2_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
 |                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | 🟡 | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                LIGHTNING_INDEXER | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                LIGHTNING_INDEXER | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                              LOG | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | ❌ |
-|                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                 MUL_MAT_HADAMARD | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 |
+|                 MUL_MAT_HADAMARD | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 |
 |                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | ❌ |
-|                  MUL_MAT_ID_W4A4 | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                  MUL_MAT_ID_W4A8 | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                     MUL_MAT_W4A4 | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                     MUL_MAT_W4A8 | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                  MUL_MAT_ID_W4A4 | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                  MUL_MAT_ID_W4A8 | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                     MUL_MAT_W4A4 | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                     MUL_MAT_W4A8 | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ |
 |                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                     OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
@@ -89,45 +89,46 @@ Legend:
 |                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                          POOL_1D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                           REPEAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
 |                      REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                         RMS_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                         RMS_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             ROPE | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                            ROUND | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                            ROUND | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                        RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                            SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                              SET | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
+|                            SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              SET | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ |
 |                         SET_ROWS | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
-|                              SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                              SIN | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
-|                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
+|                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ |
 |                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             SQRT | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         SSM_CONV | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
-|                             STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                             STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              SUM | ❌ | 🟡 | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |                         SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                     SWIGLU_CLAMP | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                     SWIGLU_CLAMP | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                            TOP_K | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                              TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                            TRUNC | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                            TOP_K | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                       TOP_K_MASK | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                              TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                            TRUNC | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                            XIELU | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
@@ -12326,8 +12326,16 @@
 "WebGPU: WebGPU","PAD_REFLECT_1D","type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9","support","0","no","WebGPU"
 "WebGPU: WebGPU","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","0","no","WebGPU"
 "WebGPU: WebGPU","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","0","no","WebGPU"
-"WebGPU: WebGPU","ARANGE","type=f32,start=0.000000,stop=10.000000,step=1.000000","support","0","no","WebGPU"
-"WebGPU: WebGPU","ARANGE","type=f32,start=0.000000,stop=1048576.000000,step=1.000000","support","0","no","WebGPU"
+"WebGPU: WebGPU","ARANGE","type=f32,start=0.000000,stop=10.000000,step=1.000000","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ARANGE","type=f32,start=3.000000,stop=3.500000,step=1.000000","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ARANGE","type=f32,start=-10.000000,stop=10.000000,step=0.300000","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ARANGE","type=f32,start=-1.000000,stop=62.750000,step=0.250000","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ARANGE","type=f32,start=-1.000000,stop=63.000000,step=0.250000","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ARANGE","type=f32,start=-1.000000,stop=63.250000,step=0.250000","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ARANGE","type=f32,start=-1.000000,stop=254.750000,step=0.250000","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ARANGE","type=f32,start=-1.000000,stop=255.000000,step=0.250000","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ARANGE","type=f32,start=-1.000000,stop=255.250000,step=0.250000","support","1","yes","WebGPU"
+"WebGPU: WebGPU","ARANGE","type=f32,start=0.000000,stop=1048576.000000,step=1.000000","support","1","yes","WebGPU"
 "WebGPU: WebGPU","TIMESTEP_EMBEDDING","type=f32,ne_a=[2,1,1,1],dim=320,max_period=10000","support","0","no","WebGPU"
 "WebGPU: WebGPU","LEAKY_RELU","type=f32,ne_a=[10,5,4,3],negative_slope=0.100000","support","0","no","WebGPU"
 "WebGPU: WebGPU","CUMSUM","type=f32,ne=[10,5,4,3]","support","1","yes","WebGPU"
@@ -4,7 +4,7 @@ project("ggml" C CXX ASM)

 ### GGML Version
 set(GGML_VERSION_MAJOR 0)
-set(GGML_VERSION_MINOR 28)
+set(GGML_VERSION_MINOR 29)
 set(GGML_VERSION_PATCH 0)
 set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")

@@ -250,6 +250,7 @@ option(GGML_OPENMP_FETCH                    "ggml: fetch LLVM OpenMP"
 option(GGML_RPC                             "ggml: use RPC"                                   OFF)
 option(GGML_SYCL                            "ggml: use SYCL"                                  OFF)
 option(GGML_SYCL_F16                        "ggml: use 16 bit floats for sycl calculations"   OFF)
+option(GGML_SYCL_SUPPORT_XMX                "ggml: build the XMX (DPAS) code paths in the SYCL backend" ON)
 option(GGML_SYCL_GRAPH                      "ggml: enable graphs in the SYCL backend"         ON)
 option(GGML_SYCL_HOST_MEM_FALLBACK          "ggml: allow host memory fallback in SYCL reorder (requires kernel 6.8+)" ON)
 option(GGML_SYCL_SUPPORT_LEVEL_ZERO_API     "ggml: use Level Zero API in SYCL backend"  ON)
@@ -70,7 +70,8 @@ extern "C" {
    GGML_API void                           ggml_backend_buffer_reset         (ggml_backend_buffer_t buffer);

    // tensor copy between different backends
-    GGML_API void ggml_backend_tensor_copy(const struct ggml_tensor * src, struct ggml_tensor * dst);
+    GGML_API void ggml_backend_tensor_copy  (const struct ggml_tensor * src, struct ggml_tensor * dst);
+    GGML_API void ggml_backend_tensor_copy_n(const struct ggml_tensor * const * srcs, struct ggml_tensor * const * dsts, size_t n_tensors);

    //
    // Backend (stream)
@@ -95,6 +96,8 @@ extern "C" {
    GGML_API void ggml_backend_tensor_get   (const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
    GGML_API void ggml_backend_tensor_set_2d(      struct ggml_tensor * tensor, const void * data, size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data);
    GGML_API void ggml_backend_tensor_get_2d(const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data);
+    GGML_API void ggml_backend_tensor_set_n (      struct ggml_tensor * const * tensors, const void * data, const size_t * offsets, const size_t * sizes, const size_t * data_offsets, size_t n_tensors);
+    GGML_API void ggml_backend_tensor_get_n (const struct ggml_tensor * const * tensors,       void * data, const size_t * offsets, const size_t * sizes, const size_t * data_offsets, size_t n_tensors);
    GGML_API void ggml_backend_tensor_memset(      struct ggml_tensor * tensor,     uint8_t value, size_t offset, size_t size);

    GGML_API void ggml_backend_synchronize(ggml_backend_t backend);
@@ -208,6 +211,18 @@ extern "C" {

    // Context management and operations for faster communication between backends, used for tensor parallelism (meta backend)
    typedef void * (*ggml_backend_comm_init_t)(ggml_backend_t * backends, size_t n_backends);
+
+    // Builds the communicator for the case where each rank is a separate process, so a process
+    // owns one backend of the communicator rather than all of them.
+    //
+    // The caller provides bootstrap_callback, which moves bytes between the ranks before the
+    // communicator exists. Each rank passes size bytes in send_buf, and recv_buf receives
+    // world_size * size bytes in rank order. The backend decides what to exchange and how many
+    // times to call it, and every rank must make the same calls in the same order.
+    // comm_init_rank blocks until all world_size ranks have joined.
+    typedef bool   (*ggml_backend_comm_bootstrap_callback)(void * user_data, const void * send_buf, void * recv_buf, size_t size);
+    typedef void * (*ggml_backend_comm_init_rank_t)(ggml_backend_t backend, int rank, int world_size, ggml_backend_comm_bootstrap_callback bootstrap_callback, void * bootstrap_callback_data);
+
    typedef void   (*ggml_backend_comm_free_t)(void * comm_ctx);
    typedef bool   (*ggml_backend_comm_allreduce_tensor_t)(void * comm_ctx, struct ggml_tensor ** tensors);

@@ -381,6 +381,8 @@ extern "C" {
    GGML_API void        ggml_fp32_to_bf16_row_ref(const float *, ggml_bf16_t *, int64_t);
    GGML_API void        ggml_fp32_to_bf16_row(const float *, ggml_bf16_t *, int64_t);

+    typedef struct { uint8_t bits; } ggml_fp8_e4m3_t;
+
    struct ggml_object;
    struct ggml_context;
    struct ggml_cgraph;
@@ -430,7 +432,8 @@ extern "C" {
        GGML_TYPE_NVFP4   = 40, // NVFP4 (4 blocks, E4M3 scale)
        GGML_TYPE_Q1_0    = 41,
        GGML_TYPE_Q2_0    = 42,
-        GGML_TYPE_COUNT   = 43,
+        GGML_TYPE_F8_E4M3 = 43,
+        GGML_TYPE_COUNT   = 44,
    };

    // [TAG_GGML_PREC]
@@ -486,6 +489,7 @@ extern "C" {
        GGML_FTYPE_MOSTLY_NVFP4   = 26, // except 1d tensors
        GGML_FTYPE_MOSTLY_Q1_0    = 27, // except 1d tensors
        GGML_FTYPE_MOSTLY_Q2_0    = 28, // except 1d tensors
+        GGML_FTYPE_MOSTLY_F8_E4M3 = 29, // except 1d tensors
    };

    // available tensor operations:
@@ -2512,6 +2516,24 @@ extern "C" {
            float                 max_bias,
            float                 logit_softcap);

+    // same as ggml_flash_attn_ext, but each q slice attends to its own list of K/V rows
+    // q:       [n_embd_k, n_batch, n_head,    ne3]
+    // k:       [n_embd_k, n_rows,  n_head_kv, 1  ] shared by all q slices
+    // v:       [n_embd_v, n_rows,  n_head_kv, 1  ]
+    // mask:    [n_kv,     n_batch, ne32,      ne3]
+    // kv_rows: [n_kv,     ne3] I32, mask column i of slice i3 uses row kv_rows[i, i3] of k and v
+    // negative rows are padding, their mask entries must be -inf
+    GGML_API struct ggml_tensor * ggml_flash_attn_ext_rows(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * q,
+            struct ggml_tensor  * k,
+            struct ggml_tensor  * v,
+            struct ggml_tensor  * mask,
+            struct ggml_tensor  * kv_rows,
+            float                 scale,
+            float                 max_bias,
+            float                 logit_softcap);
+
    GGML_DEPRECATED(GGML_API void ggml_flash_attn_ext_set_prec(
            struct ggml_tensor * a,
            enum ggml_prec       prec),
@@ -47,6 +47,9 @@ extern "C" {
    // Backend buffer
    //

+    // TODO: remove `set_tensor`, `get_tensor`, `set_tensor_2d`, `get_tensor_2d` and `cpy_tensor` from the buffer interface
+    //       they are superseded by the `set_tensor_n`, `get_tensor_n`, `cpy_tensor_n` interface
+    //       ref: https://github.com/ggml-org/llama.cpp/pull/30881#discussion_r4400299490
    struct ggml_backend_buffer_i {
        // (optional) free the buffer
        void         (*free_buffer)  (ggml_backend_buffer_t buffer);
@@ -68,6 +71,11 @@ extern "C" {
        void         (*clear)        (ggml_backend_buffer_t buffer, uint8_t value);
        // (optional) reset any internal state due to tensor initialization, such as tensor extras
        void         (*reset)        (ggml_backend_buffer_t buffer);
+        // (optional) batched tensor data copies
+        void         (*set_tensor_n) (ggml_backend_buffer_t buffer,       struct ggml_tensor * const * tensors, const void * const * data, const size_t * offsets, const size_t * sizes, size_t n_tensors);
+        void         (*get_tensor_n) (ggml_backend_buffer_t buffer, const struct ggml_tensor * const * tensors,       void * const * data, const size_t * offsets, const size_t * sizes, size_t n_tensors);
+        // (optional) batched tensor copies: returns true if all copies were performed, false if unsupported and no copies were performed
+        bool         (*cpy_tensor_n) (ggml_backend_buffer_t buffer, const struct ggml_tensor * const * srcs, struct ggml_tensor * const * dsts, size_t n_tensors);
    };

    struct ggml_backend_buffer {
@@ -489,6 +489,32 @@ static struct ggml_tensor * ggml_backend_meta_buffer_simple_tensor(const struct
    return it->second[index];
 }

+// bytes spanned by all dims except skip_dim
+static size_t ggml_backend_meta_inner_extent(const struct ggml_tensor * tensor, int skip_dim) {
+    size_t extent = 0;
+    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+        if (i != skip_dim && tensor->ne[i] > 1) {
+            extent += (size_t) (tensor->ne[i] - 1) * tensor->nb[i];
+        }
+    }
+    return extent;
+}
+
+// the view dim that holds the split axis of src, or -1
+// all other dims of src and view must stay inside one slab of that axis
+static int ggml_backend_meta_view_slab_dim(const struct ggml_tensor * view, const struct ggml_tensor * src, int axis) {
+    const size_t slab = src->nb[axis];
+    if (ggml_backend_meta_inner_extent(src, axis) >= slab) {
+        return -1;
+    }
+    for (int dim = 0; dim < GGML_MAX_DIMS; dim++) {
+        if (view->ne[dim] == src->ne[axis] && view->nb[dim] == slab) {
+            return ggml_backend_meta_inner_extent(view, dim) < slab ? dim : -1;
+        }
+    }
+    return -1;
+}
+
 static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(const struct ggml_tensor * tensor, bool assume_sync);

 static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
@@ -700,9 +726,39 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
            }
            GGML_ABORT("fatal error");
        }
+        // permuted view: split the view dim that spans the src split axis, other dims must be inside one element of it or outside the span
+        if (ggml_is_permuted(tensor) && !ggml_is_permuted(tensor->src[0]) && axis >= 0 && axis < GGML_MAX_DIMS-1) {
+            const size_t nb_axis = tensor->src[0]->nb[axis];
+            const size_t nb_next = tensor->src[0]->nb[axis+1];
+            for (int dim = 0; dim < GGML_MAX_DIMS; dim++) {
+                if (tensor->ne[dim] == 1 || tensor->nb[dim] < nb_axis || tensor->nb[dim]*tensor->ne[dim] != nb_next) {
+                    continue;
+                }
+                bool others_ok = true;
+                for (int i = 0; i < GGML_MAX_DIMS; i++) {
+                    if (i == dim || tensor->ne[i] == 1) {
+                        continue;
+                    }
+                    others_ok = others_ok && (tensor->nb[i]*tensor->ne[i] <= tensor->nb[dim] || tensor->nb[i] % nb_next == 0);
+                }
+                if (others_ok) {
+                    return {ggml_backend_meta_split_axis(dim), {0}, {1}, 1};
+                }
+            }
+        }
        if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED || src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_PARTIAL) {
            return src_ss[0];
        }
+        // a view that only regroups dims inside one slab of the split axis keeps the split,
+        // the per-device strides are only rescaled above nb[axis] (e.g. the KV streams of a permuted q)
+        if (axis >= 0 && axis < GGML_MAX_DIMS && tensor->view_offs == tensor->src[0]->view_offs) {
+            const int dim = ggml_backend_meta_view_slab_dim(tensor, tensor->src[0], axis);
+            if (dim >= 0) {
+                ggml_backend_meta_split_state ret = src_ss[0];
+                ret.axis = ggml_backend_meta_split_axis(dim);
+                return ret;
+            }
+        }
        GGML_ABORT("view of permuted tensor not implemented");
        //return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, {1}, 1};
    };
@@ -1000,8 +1056,13 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
                split_state = handle_pad(src_ss);
            } break;
            case GGML_OP_PAD_REFLECT_1D:
-            case GGML_OP_ROLL:
-            case GGML_OP_ARANGE:
+            case GGML_OP_ROLL: {
+                split_state = handle_generic(src_ss, /*scalar_only =*/ true);
+            } break;
+            case GGML_OP_ARANGE: {
+                // no sources, every device computes the full range
+                split_state = {GGML_BACKEND_SPLIT_AXIS_MIRRORED, {0}, {1}, 1};
+            } break;
            case GGML_OP_TIMESTEP_EMBEDDING: {
                split_state = handle_generic(src_ss, /*scalar_only =*/ true);
            } break;
@@ -1699,6 +1760,9 @@ static const ggml_backend_buffer_i ggml_backend_meta_buffer_iface = {
    /* .cpy_tensor      = */ nullptr,
    /* .clear           = */ ggml_backend_meta_buffer_clear,
    /* .reset           = */ ggml_backend_meta_buffer_reset,
+    /* .set_tensor_n    = */ NULL,
+    /* .get_tensor_n    = */ NULL,
+    /* .cpy_tensor_n    = */ NULL,
 };

 bool ggml_backend_buffer_is_meta(ggml_backend_buffer_t buf) {
@@ -402,6 +402,62 @@ size_t ggml_backend_get_max_size(ggml_backend_t backend) {
    return ggml_backend_buft_get_max_size(ggml_backend_get_default_buffer_type(backend));
 }

+template <typename cb_get_buffer>
+static void ggml_backend_tensor_buft_groups(size_t n_tensors, cb_get_buffer && get_buffer, std::vector<std::vector<size_t>> & groups) {
+    std::unordered_map<ggml_backend_buffer_type_t, size_t> buft_idx;
+    groups.clear();
+    for (size_t i = 0; i < n_tensors; i++) {
+        ggml_backend_buffer_t buf = get_buffer(i);
+        auto it = buft_idx.find(buf->buft);
+        if (it == buft_idx.end()) {
+            buft_idx[buf->buft] = groups.size();
+            groups.emplace_back();
+        }
+        groups[buft_idx[buf->buft]].push_back(i);
+    }
+}
+
+static bool ggml_backend_tensor_batch_is_2d(
+        const struct ggml_tensor * const * tensors,
+        const size_t * offsets,
+        const size_t * sizes,
+        const size_t * data_offsets,
+        size_t n_tensors,
+        size_t * base_offset,
+        size_t * size,
+        size_t * stride_tensor,
+        size_t * stride_data) {
+    if (n_tensors <= 1) {
+        return false;
+    }
+
+    const struct ggml_tensor * tensor = tensors[0];
+    const size_t size0 = sizes[0];
+
+    if (size0 == 0 || offsets[1] < offsets[0] || data_offsets[1] < data_offsets[0]) {
+        return false;
+    }
+
+    const size_t stride_tensor0 = offsets[1] - offsets[0];
+    const size_t stride_data0   = data_offsets[1] - data_offsets[0];
+
+    for (size_t i = 1; i < n_tensors; i++) {
+        if (tensors[i] != tensor || sizes[i] != size0) {
+            return false;
+        }
+        if (offsets[i] != offsets[0] + i*stride_tensor0 || data_offsets[i] != data_offsets[0] + i*stride_data0) {
+            return false;
+        }
+    }
+
+    *base_offset   = offsets[0];
+    *size          = size0;
+    *stride_tensor = stride_tensor0;
+    *stride_data   = stride_data0;
+
+    return true;
+}
+
 void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
    GGML_ASSERT(backend);
    GGML_ASSERT(tensor);
@@ -546,6 +602,154 @@ void ggml_backend_tensor_get_2d(const struct ggml_tensor * tensor, void * data,
    buf->iface.get_tensor_2d(buf, tensor, data, offset, size, n_copies, stride_tensor, stride_data);
 }

+void ggml_backend_tensor_set_n(struct ggml_tensor * const * tensors, const void * data,
+        const size_t * offsets, const size_t * sizes, const size_t * data_offsets, size_t n_tensors) {
+    if (n_tensors == 0) {
+        return;
+    }
+
+    std::vector<const void *> data_ptrs(n_tensors);
+    for (size_t i = 0; i < n_tensors; i++) {
+        data_ptrs[i] = (const char *) data + data_offsets[i];
+    }
+
+    ggml_backend_buffer_t buf = NULL;
+    bool same_buft = true;
+
+    for (size_t i = 0; i < n_tensors; i++) {
+        GGML_ASSERT(tensors[i] != NULL);
+        ggml_backend_buffer_t tensor_buf = tensors[i]->view_src ? tensors[i]->view_src->buffer : tensors[i]->buffer;
+        GGML_ASSERT(tensor_buf != NULL && "tensor buffer not set");
+        GGML_ASSERT(tensors[i]->data != NULL && "tensor not allocated");
+        GGML_ASSERT(offsets[i] + sizes[i] <= ggml_nbytes(tensors[i]) && "tensor write out of bounds");
+
+        if (buf == NULL) {
+            buf = tensor_buf;
+        } else if (buf->buft != tensor_buf->buft) {
+            same_buft = false;
+        }
+    }
+
+    if (same_buft && buf->iface.set_tensor_n != NULL) {
+        buf->iface.set_tensor_n(buf, tensors, data_ptrs.data(), offsets, sizes, n_tensors);
+        return;
+    }
+
+    if (!same_buft) {
+        std::vector<std::vector<size_t>> groups;
+        ggml_backend_tensor_buft_groups(n_tensors, [&](size_t i) {
+            return tensors[i]->view_src ? tensors[i]->view_src->buffer : tensors[i]->buffer;
+        }, groups);
+
+        for (size_t g = 0; g < groups.size(); g++) {
+            std::vector<ggml_tensor *> g_tensors;
+            std::vector<size_t> g_offsets;
+            std::vector<size_t> g_sizes;
+            std::vector<size_t> g_data_offsets;
+            g_tensors.reserve(groups[g].size());
+            g_offsets.reserve(groups[g].size());
+            g_sizes.reserve(groups[g].size());
+            g_data_offsets.reserve(groups[g].size());
+
+            for (size_t i : groups[g]) {
+                g_tensors.push_back(tensors[i]);
+                g_offsets.push_back(offsets[i]);
+                g_sizes.push_back(sizes[i]);
+                g_data_offsets.push_back(data_offsets[i]);
+            }
+
+            ggml_backend_tensor_set_n(g_tensors.data(), data, g_offsets.data(), g_sizes.data(), g_data_offsets.data(), g_tensors.size());
+        }
+        return;
+    }
+
+    size_t base_offset = 0, size = 0, stride_tensor = 0, stride_data = 0;
+    if (ggml_backend_tensor_batch_is_2d(tensors, offsets, sizes, data_offsets, n_tensors,
+                &base_offset, &size, &stride_tensor, &stride_data)) {
+        ggml_backend_tensor_set_2d(tensors[0], data_ptrs[0], base_offset, size,
+                n_tensors, stride_tensor, stride_data);
+        return;
+    }
+
+    for (size_t i = 0; i < n_tensors; i++) {
+        ggml_backend_tensor_set(tensors[i], data_ptrs[i], offsets[i], sizes[i]);
+    }
+}
+
+void ggml_backend_tensor_get_n(const struct ggml_tensor * const * tensors, void * data,
+        const size_t * offsets, const size_t * sizes, const size_t * data_offsets, size_t n_tensors) {
+    if (n_tensors == 0) {
+        return;
+    }
+
+    std::vector<void *> data_ptrs(n_tensors);
+    for (size_t i = 0; i < n_tensors; i++) {
+        data_ptrs[i] = (char *) data + data_offsets[i];
+    }
+
+    ggml_backend_buffer_t buf = NULL;
+    bool same_buft = true;
+
+    for (size_t i = 0; i < n_tensors; i++) {
+        GGML_ASSERT(tensors[i] != NULL);
+        ggml_backend_buffer_t tensor_buf = tensors[i]->view_src ? tensors[i]->view_src->buffer : tensors[i]->buffer;
+        GGML_ASSERT(tensor_buf != NULL && "tensor buffer not set");
+        GGML_ASSERT(tensors[i]->data != NULL && "tensor not allocated");
+        GGML_ASSERT(offsets[i] + sizes[i] <= ggml_nbytes(tensors[i]) && "tensor read out of bounds");
+
+        if (buf == NULL) {
+            buf = tensor_buf;
+        } else if (buf->buft != tensor_buf->buft) {
+            same_buft = false;
+        }
+    }
+
+    if (same_buft && buf->iface.get_tensor_n != NULL) {
+        buf->iface.get_tensor_n(buf, tensors, data_ptrs.data(), offsets, sizes, n_tensors);
+        return;
+    }
+
+    if (!same_buft) {
+        std::vector<std::vector<size_t>> groups;
+        ggml_backend_tensor_buft_groups(n_tensors, [&](size_t i) {
+            return tensors[i]->view_src ? tensors[i]->view_src->buffer : tensors[i]->buffer;
+        }, groups);
+
+        for (size_t g = 0; g < groups.size(); g++) {
+            std::vector<const ggml_tensor *> g_tensors;
+            std::vector<size_t> g_offsets;
+            std::vector<size_t> g_sizes;
+            std::vector<size_t> g_data_offsets;
+            g_tensors.reserve(groups[g].size());
+            g_offsets.reserve(groups[g].size());
+            g_sizes.reserve(groups[g].size());
+            g_data_offsets.reserve(groups[g].size());
+
+            for (size_t i : groups[g]) {
+                g_tensors.push_back(tensors[i]);
+                g_offsets.push_back(offsets[i]);
+                g_sizes.push_back(sizes[i]);
+                g_data_offsets.push_back(data_offsets[i]);
+            }
+
+            ggml_backend_tensor_get_n(g_tensors.data(), data, g_offsets.data(), g_sizes.data(), g_data_offsets.data(), g_tensors.size());
+        }
+        return;
+    }
+
+    size_t base_offset = 0, size = 0, stride_tensor = 0, stride_data = 0;
+    if (ggml_backend_tensor_batch_is_2d(tensors, offsets, sizes, data_offsets, n_tensors,
+                &base_offset, &size, &stride_tensor, &stride_data)) {
+        ggml_backend_tensor_get_2d(tensors[0], data_ptrs[0], base_offset, size,
+                n_tensors, stride_tensor, stride_data);
+        return;
+    }
+
+    for (size_t i = 0; i < n_tensors; i++) {
+        ggml_backend_tensor_get(tensors[i], data_ptrs[i], offsets[i], sizes[i]);
+    }
+}
+
 void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
    GGML_ASSERT(tensor);
    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
@@ -648,6 +852,59 @@ void ggml_backend_tensor_copy(const struct ggml_tensor * src, struct ggml_tensor
    }
 }

+void ggml_backend_tensor_copy_n(const struct ggml_tensor * const * srcs, struct ggml_tensor * const * dsts, size_t n_tensors) {
+    if (n_tensors == 0) {
+        return;
+    }
+
+    for (size_t i = 0; i < n_tensors; ++i) {
+        GGML_ASSERT(ggml_are_same_layout(srcs[i], dsts[i]) && "cannot copy tensors with different layouts");
+    }
+
+    ggml_backend_buffer_t buf = dsts[0]->view_src ? dsts[0]->view_src->buffer : dsts[0]->buffer;
+    bool same_buft = true;
+    for (size_t i = 0; i < n_tensors; ++i) {
+        ggml_backend_buffer_t dst_buf = dsts[i]->view_src ? dsts[i]->view_src->buffer : dsts[i]->buffer;
+        if (dst_buf->buft != buf->buft) {
+            same_buft = false;
+            break;
+        }
+    }
+
+    // if cpy_tensor_n returns false, it must not have performed any copies
+    if (same_buft && buf->iface.cpy_tensor_n != NULL) {
+        if (buf->iface.cpy_tensor_n(buf, srcs, dsts, n_tensors)) {
+            return;
+        }
+    }
+
+    if (!same_buft) {
+        std::vector<std::vector<size_t>> groups;
+        ggml_backend_tensor_buft_groups(n_tensors, [&](size_t i) {
+            return dsts[i]->view_src ? dsts[i]->view_src->buffer : dsts[i]->buffer;
+        }, groups);
+
+        for (size_t g = 0; g < groups.size(); g++) {
+            std::vector<const ggml_tensor *> g_srcs;
+            std::vector<ggml_tensor *> g_dsts;
+            g_srcs.reserve(groups[g].size());
+            g_dsts.reserve(groups[g].size());
+
+            for (size_t i : groups[g]) {
+                g_srcs.push_back(srcs[i]);
+                g_dsts.push_back(dsts[i]);
+            }
+
+            ggml_backend_tensor_copy_n(g_srcs.data(), g_dsts.data(), g_srcs.size());
+        }
+        return;
+    }
+
+    for (size_t i = 0; i < n_tensors; ++i) {
+        ggml_backend_tensor_copy(srcs[i], dsts[i]);
+    }
+}
+
 void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");

@@ -853,6 +1110,9 @@ static const struct ggml_backend_buffer_i ggml_backend_multi_buffer_i = {
    /* .cpy_tensor      = */ NULL,
    /* .clear           = */ ggml_backend_multi_buffer_clear,
    /* .reset           = */ NULL,
+    /* .set_tensor_n    = */ NULL,
+    /* .get_tensor_n    = */ NULL,
+    /* .cpy_tensor_n    = */ NULL,
 };

 ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer_t * buffers, size_t n_buffers) {
@@ -2532,6 +2792,9 @@ static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_i = {
    /* .cpy_tensor      = */ ggml_backend_cpu_buffer_cpy_tensor,
    /* .clear           = */ ggml_backend_cpu_buffer_clear,
    /* .reset           = */ NULL,
+    /* .set_tensor_n    = */ NULL,
+    /* .get_tensor_n    = */ NULL,
+    /* .cpy_tensor_n    = */ NULL,
 };

 static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_from_ptr_i = {
@@ -2546,6 +2809,9 @@ static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_from_ptr_i = {
    /* .cpy_tensor      = */ ggml_backend_cpu_buffer_cpy_tensor,
    /* .clear           = */ ggml_backend_cpu_buffer_clear,
    /* .reset           = */ NULL,
+    /* .set_tensor_n    = */ NULL,
+    /* .get_tensor_n    = */ NULL,
+    /* .cpy_tensor_n    = */ NULL,
 };

 // CPU backend buffer type
@@ -1478,6 +1478,9 @@ static const ggml_backend_buffer_i ggml_backend_cann_buffer_interface = {
    /* .cpy_tensor      = */ ggml_backend_cann_buffer_cpy_tensor,
    /* .clear           = */ ggml_backend_cann_buffer_clear,
    /* .reset           = */ NULL,
+    /* .set_tensor_n    = */ NULL,
+    /* .get_tensor_n    = */ NULL,
+    /* .cpy_tensor_n    = */ NULL,
 };

 /**
@@ -2667,6 +2670,10 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
            return true;
        case GGML_OP_FLASH_ATTN_EXT:
            {
+                // K/V row lists (ggml_flash_attn_ext_rows) are not supported
+                if (op->src[5] != nullptr) {
+                    return false;
+                }
 #ifdef ASCEND_310P
                // FA not support on 310p device
                return false;
@@ -116,6 +116,9 @@ static ggml_backend_buffer_i ggml_backend_amx_buffer_interface = {
    /* .cpy_tensor      = */ nullptr,
    /* .clear           = */ ggml_backend_amx_buffer_clear,
    /* .reset           = */ nullptr,
+    /* .set_tensor_n    = */ NULL,
+    /* .get_tensor_n    = */ NULL,
+    /* .cpy_tensor_n    = */ NULL,
 };

 static const char * ggml_backend_amx_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
@@ -29,7 +29,7 @@ void ggml_quantize_mat_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTR
    assert(k % QK8_0 == 0);
    const int nb = k / QK8_0;

-#if defined(__riscv_v_intrinsic)
+#if defined(__riscv_v)
    block_q8_0x4 * GGML_RESTRICT y = (block_q8_0x4 *) vy;
    const size_t vl_calc = __riscv_vsetvl_e32m8(QK8_0);
    const size_t vl_save = __riscv_vsetvl_e64m2(4);
@@ -43,6 +43,14 @@ static inline float f32_to_f32(float x) {
    return x;
 }

+static inline float f8_e4m3_to_f32(ggml_fp8_e4m3_t x) {
+    return ggml_f8_e4m3_to_fp32(x.bits);
+}
+
+static inline ggml_fp8_e4m3_t f32_to_f8_e4m3(float x) {
+    return { ggml_fp32_to_f8_e4m3(x) };
+}
+
 // TODO - merge this into the traits table, after using row-based conversions
 template <class T>
 struct type_conversion_table;
@@ -65,6 +73,12 @@ struct type_conversion_table<ggml_bf16_t> {
    static constexpr ggml_bf16_t (*from_f32)(float) = f32_to_bf16;
 };

+template <>
+struct type_conversion_table<ggml_fp8_e4m3_t> {
+    static constexpr float (*to_f32)(ggml_fp8_e4m3_t) = f8_e4m3_to_f32;
+    static constexpr ggml_fp8_e4m3_t (*from_f32)(float) = f32_to_f8_e4m3;
+};
+
 template <>
 struct type_conversion_table<int32_t> {
    static constexpr float (*to_f32)(int32_t) = i32_to_f32;
@@ -30,7 +30,7 @@ struct ggml_compute_params {
 };


-#if defined(_MSC_VER)
+#if defined(_MSC_VER) && !defined(__clang__)

 #define m512bh(p) p
 #define m512i(p) p
@@ -345,7 +345,7 @@ static inline int32x4_t ggml_nvfp4_dot8(const int8x8_t q4_lo, const int8x8_t q8_
 #include <immintrin.h>
 #endif

-#ifdef __riscv_v_intrinsic
+#if defined(__riscv_v)
 #include <riscv_vector.h>
 #endif

@@ -86,6 +86,9 @@ float ggml_table_f32_e8m0_half[1 << 8];
 // precomputed f32 table for ue4m3 (1 KB) (simd-mappings.h)
 float ggml_table_f32_ue4m3[1 << 8];

+// Signed FP8 needs its own table: UE4M3 ignores the sign, halves values for NVFP4, and maps NaN to zero.
+float ggml_table_f32_f8_e4m3[1 << 8];
+
 #if defined(__ARM_ARCH)
 struct ggml_arm_arch_features_type {
    int sve_cnt;
@@ -296,6 +299,12 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = {
        .vec_dot_type             = GGML_TYPE_Q8_0,
        .nrows                    = 1,
    },
+    [GGML_TYPE_F8_E4M3] = {
+        .from_float               = quantize_row_f8_e4m3,
+        .vec_dot                  = ggml_vec_dot_f8_e4m3_f32,
+        .vec_dot_type             = GGML_TYPE_F32,
+        .nrows                    = 1,
+    },
    [GGML_TYPE_Q2_K] = {
        .from_float               = quantize_row_q2_K,
        .vec_dot                  = ggml_vec_dot_q2_K_q8_K,
@@ -737,7 +746,7 @@ static void ggml_init_arm_arch_features(void) {}
 #endif
 #endif // __ARM_ARCH

-#if defined(__riscv) && defined(__riscv_v_intrinsic)
+#if defined(__riscv) && defined(__riscv_v)
 #include <riscv_vector.h>
 static void ggml_init_riscv_arch_features(void) {
    ggml_riscv_arch_features.rvv_vlen = __riscv_vlenb();
@@ -2876,7 +2885,7 @@ struct ggml_cplan ggml_graph_plan(
                case GGML_OP_ADD_ID:
                case GGML_OP_ADD1:
                    {
-                        if (ggml_is_quantized(node->src[0]->type)) {
+                        if (ggml_is_quantized(node->src[0]->type) || node->src[0]->type == GGML_TYPE_F8_E4M3) {
                            cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
                        }
                    } break;
@@ -2924,7 +2933,7 @@ struct ggml_cplan ggml_graph_plan(
                    } break;
                case GGML_OP_OUT_PROD:
                    {
-                        if (ggml_is_quantized(node->src[0]->type) ||
+                        if (ggml_is_quantized(node->src[0]->type) || node->src[0]->type == GGML_TYPE_F8_E4M3 ||
                            node->src[0]->type == GGML_TYPE_F16) {
                            cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
                        }
@@ -3547,7 +3556,7 @@ void ggml_cpu_fp16_to_fp32(const ggml_fp16_t * x, float * y, int64_t n) {
        _mm_storeu_ps(y + i, y_vec);
    }

-#elif defined(__riscv_v_intrinsic) && defined(__riscv_zvfhmin)
+#elif defined(__riscv_v) && defined(__riscv_zvfhmin)
    // calculate step size
    const int epr = __riscv_vsetvlmax_e16m2();
    const int step = epr * 2;
@@ -3617,7 +3626,7 @@ void ggml_cpu_bf16_to_fp32(const ggml_bf16_t * x, float * y, int64_t n) {
                                        (const __m128i *)(x + i))),
                                16)));
    }
-#elif defined(__riscv_v_intrinsic) && defined(__riscv_zvfbfmin)
+#elif defined(__riscv_v) && defined(__riscv_zvfbfmin)
    // calculate step size
    const int epr = __riscv_vsetvlmax_e16m2();
    const int step = epr * 2;
@@ -3737,7 +3746,7 @@ int ggml_cpu_has_arm_fma(void) {
 }

 int ggml_cpu_has_riscv_v(void) {
-#if defined(__riscv_v_intrinsic)
+#if defined(__riscv_v)
    return 1;
 #else
    return 0;
@@ -3745,7 +3754,7 @@ int ggml_cpu_has_riscv_v(void) {
 }

 int ggml_cpu_get_rvv_vlen(void) {
-#if defined(__riscv) && defined(__riscv_v_intrinsic)
+#if defined(__riscv) && defined(__riscv_v)
    return ggml_riscv_arch_features.rvv_vlen;
 #else
    return 0;
@@ -3908,6 +3917,7 @@ void ggml_cpu_init(void) {
            // initialize UE4M3 table (256 entries)
            for (int i = 0; i < (1 << 8); ++i) {
                ggml_table_f32_ue4m3[i] = ggml_ue4m3_to_fp32(i);
+                ggml_table_f32_f8_e4m3[i] = ggml_f8_e4m3_to_fp32(i);
            }

            const uint64_t t_end = ggml_time_us(); UNUSED(t_end);
@@ -69,7 +69,7 @@
 #define VECTOR_REGISTERS 16
 #endif

-#if defined(__riscv_v_intrinsic)
+#if defined(__riscv_v)
 #define LMUL 4
 #endif

@@ -180,7 +180,7 @@ inline float32x4_t madd(float32x4_t a, float32x4_t b, float32x4_t c) {
 }
 #endif

-#if defined(__riscv_v_intrinsic)
+#if defined(__riscv_v)
 template <> inline vfloat32m1_t madd(vfloat32m1_t a, vfloat32m1_t b, vfloat32m1_t c) {
    return __riscv_vfmacc_vv_f32m1(c, a, b, __riscv_vsetvlmax_e32m1());
 }
@@ -277,7 +277,7 @@ inline float hsum(__m512 x) {
 }
 #endif // __AVX512F__

-#if defined(__riscv_v_intrinsic)
+#if defined(__riscv_v)
 inline float hsum(vfloat32m1_t x) {
    return __riscv_vfmv_f_s_f32m1_f32(
        __riscv_vfredusum_vs_f32m1_f32m1(x, __riscv_vfmv_v_f_f32m1(0, 1), __riscv_vsetvlmax_e32m1()));
@@ -458,7 +458,7 @@ template <> inline __m512bh load_partial(const ggml_bf16_t *p, int n) {
 #endif
 #endif

-#if defined(__riscv_v_intrinsic)
+#if defined(__riscv_v)
 template <> inline vfloat32m1_t load(const float *p) {
    return __riscv_vle32_v_f32m1(p, __riscv_vsetvlmax_e32m1());
 }
@@ -503,7 +503,7 @@ template <> inline vbfloat16m4_t load(const ggml_bf16_t *p) {
 }
 #endif

-#if defined(__riscv_v_intrinsic)
+#if defined(__riscv_v)
 template <typename T> T set_zero();

 template <> inline vfloat32m1_t set_zero() {
@@ -520,7 +520,7 @@ template <> inline vfloat32m8_t set_zero() {
 }
 #endif

-#if defined(__riscv_v_intrinsic)
+#if defined(__riscv_v)
 template <typename T> size_t vlmax() {
    if constexpr (std::is_same_v<T, vfloat32m1_t>) { return  __riscv_vsetvlmax_e32m1(); }
    else if constexpr (std::is_same_v<T, vfloat32m2_t>) { return  __riscv_vsetvlmax_e32m2(); }
@@ -732,7 +732,7 @@ class tinyBLAS {
    const int64_t ldc;
 };

-#if defined(__riscv_v_intrinsic)
+#if defined(__riscv_v)
 template <typename D, typename V, typename TA, typename TB, typename TC>
 class tinyBLAS_RVV {
  public:
@@ -3958,7 +3958,7 @@ bool llamafile_sgemm(const struct ggml_compute_params * params, int64_t m, int64
            params->ith, params->nth};
        tb.matmul(m, n);
        return true;
-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)
    #if LMUL == 1
        tinyBLAS_RVV<vfloat32m1_t, vfloat32m1_t, float, float, float> tb{ params,
            k, (const float *)A, lda,
@@ -537,26 +537,36 @@ void ggml_compute_forward_dup(
    switch (src0->type) {
        case GGML_TYPE_F16:
            {
-                /**/ if (dst->type == GGML_TYPE_F16)  ggml_compute_forward_dup_flt<ggml_fp16_t, ggml_fp16_t>(params, dst);
-                else if (dst->type == GGML_TYPE_BF16) ggml_compute_forward_dup_flt<ggml_fp16_t, ggml_bf16_t>(params, dst);
-                else if (dst->type == GGML_TYPE_F32)  ggml_compute_forward_dup_flt<ggml_fp16_t, float      >(params, dst);
+                /**/ if (dst->type == GGML_TYPE_F16)     ggml_compute_forward_dup_flt<ggml_fp16_t, ggml_fp16_t>(params, dst);
+                else if (dst->type == GGML_TYPE_BF16)    ggml_compute_forward_dup_flt<ggml_fp16_t, ggml_bf16_t>(params, dst);
+                else if (dst->type == GGML_TYPE_F32)     ggml_compute_forward_dup_flt<ggml_fp16_t, float      >(params, dst);
+                else if (dst->type == GGML_TYPE_F8_E4M3) ggml_compute_forward_dup_flt<ggml_fp16_t, ggml_fp8_e4m3_t>(params, dst);
                else ggml_compute_forward_dup_to_q<ggml_fp16_t>(params, dst);
            } break;
        case GGML_TYPE_BF16:
            {
-                /**/ if (dst->type == GGML_TYPE_F16)  ggml_compute_forward_dup_flt<ggml_bf16_t, ggml_fp16_t>(params, dst);
-                else if (dst->type == GGML_TYPE_BF16) ggml_compute_forward_dup_flt<ggml_bf16_t, ggml_bf16_t>(params, dst);
-                else if (dst->type == GGML_TYPE_F32)  ggml_compute_forward_dup_flt<ggml_bf16_t, float      >(params, dst);
+                /**/ if (dst->type == GGML_TYPE_F16)     ggml_compute_forward_dup_flt<ggml_bf16_t, ggml_fp16_t>(params, dst);
+                else if (dst->type == GGML_TYPE_BF16)    ggml_compute_forward_dup_flt<ggml_bf16_t, ggml_bf16_t>(params, dst);
+                else if (dst->type == GGML_TYPE_F32)     ggml_compute_forward_dup_flt<ggml_bf16_t, float      >(params, dst);
+                else if (dst->type == GGML_TYPE_F8_E4M3) ggml_compute_forward_dup_flt<ggml_bf16_t, ggml_fp8_e4m3_t>(params, dst);
                else ggml_compute_forward_dup_to_q<ggml_bf16_t>(params, dst);
            } break;
        case GGML_TYPE_F32:
            {
-                /**/ if (dst->type == GGML_TYPE_F16)  ggml_compute_forward_dup_flt<float, ggml_fp16_t>(params, dst);
-                else if (dst->type == GGML_TYPE_BF16) ggml_compute_forward_dup_flt<float, ggml_bf16_t>(params, dst);
-                else if (dst->type == GGML_TYPE_F32)  ggml_compute_forward_dup_flt<float, float      >(params, dst);
-                else if (dst->type == GGML_TYPE_I32)  ggml_compute_forward_dup_flt<float, int32_t    >(params, dst);
+                /**/ if (dst->type == GGML_TYPE_F16)     ggml_compute_forward_dup_flt<float, ggml_fp16_t>(params, dst);
+                else if (dst->type == GGML_TYPE_BF16)    ggml_compute_forward_dup_flt<float, ggml_bf16_t>(params, dst);
+                else if (dst->type == GGML_TYPE_F32)     ggml_compute_forward_dup_flt<float, float      >(params, dst);
+                else if (dst->type == GGML_TYPE_F8_E4M3) ggml_compute_forward_dup_flt<float, ggml_fp8_e4m3_t>(params, dst);
+                else if (dst->type == GGML_TYPE_I32)     ggml_compute_forward_dup_flt<float, int32_t    >(params, dst);
                else ggml_compute_forward_dup_to_q<float>(params, dst);
            } break;
+        case GGML_TYPE_F8_E4M3:
+            {
+                /**/ if (dst->type == GGML_TYPE_F16)  ggml_compute_forward_dup_flt<ggml_fp8_e4m3_t, ggml_fp16_t>(params, dst);
+                else if (dst->type == GGML_TYPE_BF16) ggml_compute_forward_dup_flt<ggml_fp8_e4m3_t, ggml_bf16_t>(params, dst);
+                else if (dst->type == GGML_TYPE_F32)  ggml_compute_forward_dup_flt<ggml_fp8_e4m3_t, float      >(params, dst);
+                else GGML_ABORT("not implemented");
+            } break;
        case GGML_TYPE_I32:
            {
                if (dst->type == GGML_TYPE_F32) ggml_compute_forward_dup_flt<int32_t, float>(params, dst);
@@ -605,8 +615,9 @@ static void ggml_compute_forward_add_q_f32(
    GGML_ASSERT(nb1 <= nb2);
    GGML_ASSERT(nb2 <= nb3);

-    GGML_ASSERT(ggml_is_quantized(src0->type));
+    GGML_ASSERT(ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F8_E4M3);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(ne00 % ggml_blck_size(type) == 0);

    // rows per thread
    const int dr = (nr + nth - 1)/nth;
@@ -636,8 +647,6 @@ static void ggml_compute_forward_add_q_f32(
        float * src1_row = (float *)((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13));
        void  * dst_row  = (void *) ((char *)  dst->data + ( i1*nb1  +  i2*nb2  +  i3*nb3));

-        assert(ne00 % 32 == 0);
-
        // unquantize row from src0 to temp buffer
        dequantize_row_q(src0_row, wdata, ne00);
        // add src1
@@ -673,6 +682,7 @@ void ggml_compute_forward_add(
        case GGML_TYPE_Q8_0:
        case GGML_TYPE_MXFP4:
        case GGML_TYPE_NVFP4:
+        case GGML_TYPE_F8_E4M3:
        case GGML_TYPE_Q2_K:
        case GGML_TYPE_Q3_K:
        case GGML_TYPE_Q4_K:
@@ -955,7 +965,7 @@ static void ggml_compute_forward_add1_q_f32(
    GGML_ASSERT(nb1 <= nb2);
    GGML_ASSERT(nb2 <= nb3);

-    GGML_ASSERT(ggml_is_quantized(src0->type));
+    GGML_ASSERT(ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F8_E4M3);
    GGML_ASSERT(dst->type == src0->type);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);

@@ -1130,6 +1140,7 @@ void ggml_compute_forward_add1(
        case GGML_TYPE_Q8_1:
        case GGML_TYPE_MXFP4:
        case GGML_TYPE_NVFP4:
+        case GGML_TYPE_F8_E4M3:
        case GGML_TYPE_Q2_K:
        case GGML_TYPE_Q3_K:
        case GGML_TYPE_Q4_K:
@@ -1261,6 +1272,7 @@ void ggml_compute_forward_acc(
        case GGML_TYPE_Q8_1:
        case GGML_TYPE_MXFP4:
        case GGML_TYPE_NVFP4:
+        case GGML_TYPE_F8_E4M3:
        case GGML_TYPE_Q2_K:
        case GGML_TYPE_Q3_K:
        case GGML_TYPE_Q4_K:
@@ -4804,6 +4816,7 @@ void ggml_compute_forward_out_prod(
        case GGML_TYPE_Q8_0:
        case GGML_TYPE_MXFP4:
        case GGML_TYPE_NVFP4:
+        case GGML_TYPE_F8_E4M3:
        case GGML_TYPE_Q2_K:
        case GGML_TYPE_Q3_K:
        case GGML_TYPE_Q4_K:
@@ -5129,6 +5142,7 @@ void ggml_compute_forward_set(
        case GGML_TYPE_Q8_1:
        case GGML_TYPE_MXFP4:
        case GGML_TYPE_NVFP4:
+        case GGML_TYPE_F8_E4M3:
        case GGML_TYPE_Q2_K:
        case GGML_TYPE_Q3_K:
        case GGML_TYPE_Q4_K:
@@ -5354,6 +5368,7 @@ void ggml_compute_forward_get_rows(
        case GGML_TYPE_Q8_1:
        case GGML_TYPE_MXFP4:
        case GGML_TYPE_NVFP4:
+        case GGML_TYPE_F8_E4M3:
        case GGML_TYPE_Q2_K:
        case GGML_TYPE_Q3_K:
        case GGML_TYPE_Q4_K:
@@ -6114,6 +6129,7 @@ void ggml_compute_forward_clamp(
        case GGML_TYPE_Q8_1:
        case GGML_TYPE_MXFP4:
        case GGML_TYPE_NVFP4:
+        case GGML_TYPE_F8_E4M3:
        case GGML_TYPE_Q2_K:
        case GGML_TYPE_Q3_K:
        case GGML_TYPE_Q4_K:
@@ -8828,6 +8844,7 @@ static void ggml_compute_forward_flash_attn_ext_f16_one_chunk(
    const ggml_tensor * v     = dst->src[2];
    const ggml_tensor * mask  = dst->src[3];
    const ggml_tensor * sinks = dst->src[4];
+    const ggml_tensor * kv_rows = dst->src[5];

    GGML_TENSOR_LOCALS(int64_t, neq, q,   ne)
    GGML_TENSOR_LOCALS(size_t,  nbq, q,   nb)
@@ -8935,6 +8952,8 @@ static void ggml_compute_forward_flash_attn_ext_f16_one_chunk(
        const float * pq = (const float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3));
        q_to_vec_dot(pq, Q_q, DK);

+        const int32_t * rows = kv_rows ? (const int32_t *) ((const char *) kv_rows->data + iq3*kv_rows->nb[1]) : nullptr;
+
        // online softmax / attention
        // loop over n_kv and n_head_kv
        // ref: https://arxiv.org/pdf/2112.05682.pdf
@@ -8945,9 +8964,14 @@ static void ggml_compute_forward_flash_attn_ext_f16_one_chunk(
                continue;
            }

+            const int64_t ik1 = rows ? rows[ic] : ic;
+            if (ik1 < 0) {
+                continue;
+            }
+
            float s; // KQ value

-            const char * k_data = (const char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3);
+            const char * k_data = (const char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3);
            kq_vec_dot(DK, &s, 0, k_data, 0, Q_q, 0, 1);

            s = s*scale; // scale KQ value
@@ -8963,7 +8987,7 @@ static void ggml_compute_forward_flash_attn_ext_f16_one_chunk(
            float ms = 1.0f; // upon new higher max val, scale VKQ and KQ sum with this value
            float vs = 1.0f; // post-softmax KQ value, expf(s - M)

-            const char * v_data = ((const char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3));
+            const char * v_data = ((const char *) v->data + (ik1*nbv1 + iv2*nbv2 + iv3*nbv3));

            if (v->type == GGML_TYPE_F16) {
                if (s > M) {
@@ -9130,6 +9154,10 @@ static void ggml_compute_forward_flash_attn_ext_tiled(
    static constexpr int Q_TILE_SZ  = ggml_fa_tile_config::Q;
    static constexpr int KV_TILE_SZ = ggml_fa_tile_config::KV;

+    // with kv_rows the loop runs over the mask columns and reads K/V rows through kv_rows
+    const ggml_tensor * kv_rows = dst->src[5];
+    const int64_t n_kv_cols = kv_rows ? kv_rows->ne[0] : nek1;
+
    int ir = ir0;
    while (ir < ir1) {
        // q indices for the start of this tile
@@ -9137,6 +9165,10 @@ static void ggml_compute_forward_flash_attn_ext_tiled(
        const int iq2 = (ir - iq3*neq2*neq1)/neq1;
        const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1);

+        // padding rows are masked, read row 0 for them
+        const int32_t * rows = kv_rows ? (const int32_t *) ((const char *) kv_rows->data + iq3*kv_rows->nb[1]) : nullptr;
+        auto kv_row = [rows](int64_t i) -> int64_t { return rows ? std::max<int32_t>(rows[i], 0) : i; };
+
        // Number of valid rows in this tile:
        // - limited by tile size (Q_TILE_SZ)
        // - limited by chunk boundary (ir1 - ir)
@@ -9196,8 +9228,8 @@ static void ggml_compute_forward_flash_attn_ext_tiled(
        memset(K_f32, 0, DK * KV_TILE_SZ * sizeof(float));
        memset(V32,   0, KV_TILE_SZ * DV * sizeof(float));

-        for (int64_t ic = 0; ic < nek1; ic += KV_TILE_SZ) {
-            const int kv_tile = (int)std::min((int64_t)KV_TILE_SZ, nek1 - ic);
+        for (int64_t ic = 0; ic < n_kv_cols; ic += KV_TILE_SZ) {
+            const int kv_tile = (int)std::min((int64_t)KV_TILE_SZ, n_kv_cols - ic);

            // skip the tile entirely if all the masks are -inf
            if (mask) {
@@ -9224,7 +9256,7 @@ static void ggml_compute_forward_flash_attn_ext_tiled(
            // Pack K tile transposed: K_f32[dk][kv] so KV_TILE is contiguous (SIMD dim)
            // Zero-pad the last tile so the GEMM always operates on KV_TILE_SZ columns
            for (int tk = 0; tk < kv_tile; tk++) {
-                const char * k_data = (const char *)k->data + (ic + tk)*nbk1 + ik2*nbk2 + ik3*nbk3;
+                const char * k_data = (const char *)k->data + kv_row(ic + tk)*nbk1 + ik2*nbk2 + ik3*nbk3;
                if (kv_type == GGML_TYPE_F16) {
                    const ggml_fp16_t * k_f16 = (const ggml_fp16_t *)k_data;
                    for (int64_t dk = 0; dk < DK; dk++) {
@@ -9289,7 +9321,7 @@ static void ggml_compute_forward_flash_attn_ext_tiled(
            // V accumulation: VKQ32 += softmax(KQ) * V
            // Pack V tile to contiguous F32, zero-padded
            for (int tk = 0; tk < kv_tile; tk++) {
-                const char * v_data = (const char *)v->data + (ic + tk)*nbv1 + iv2*nbv2 + iv3*nbv3;
+                const char * v_data = (const char *)v->data + kv_row(ic + tk)*nbv1 + iv2*nbv2 + iv3*nbv3;
                if (kv_type == GGML_TYPE_F16) {
                    ggml_cpu_fp16_to_fp32((const ggml_fp16_t *)v_data, V32 + tk * DV, DV);
                } else {
@@ -9461,8 +9493,12 @@ static void ggml_compute_forward_flash_attn_ext_f16(
    // When use_ref is set, force the vec-only reference implementation (no tiling, no KV-chunking)
    const bool use_ref = params->use_ref;

+    // with kv_rows the loops run over the mask columns, the split KV path does not support it
+    const ggml_tensor * kv_rows = dst->src[5];
+    const int64_t n_kv_cols = kv_rows ? kv_rows->ne[0] : nek1;
+
    const bool kv_is_f32_or_f16 = (k->type == GGML_TYPE_F32 || k->type == GGML_TYPE_F16);
-    const bool use_split_kv_path = !use_ref && (neq1 == 1 && neq3 == 1) && kv_is_f32_or_f16 && (k->type == v->type) && q->type == GGML_TYPE_F32 && nek1 >= 512;
+    const bool use_split_kv_path = !use_ref && !kv_rows && (neq1 == 1 && neq3 == 1) && kv_is_f32_or_f16 && (k->type == v->type) && q->type == GGML_TYPE_F32 && nek1 >= 512;

    if (use_split_kv_path) {
        const int64_t chunk_size = (nek1 + nth - 1) / nth;
@@ -9541,7 +9577,7 @@ static void ggml_compute_forward_flash_attn_ext_f16(
            if (use_tiled) {
                ggml_compute_forward_flash_attn_ext_tiled(params, dst, ir0, ir1);
            } else {
-                ggml_compute_forward_flash_attn_ext_f16_one_chunk(params, dst, ir0, ir1, 0, nek1, nullptr, 0);
+                ggml_compute_forward_flash_attn_ext_f16_one_chunk(params, dst, ir0, ir1, 0, n_kv_cols, nullptr, 0);
            }

            current_chunk = ggml_threadpool_chunk_add(params->threadpool, 1);
@@ -10078,7 +10114,7 @@ static void ggml_compute_forward_ssm_scan_f32(
                        }

                        sumf = GGML_F32xt_REDUCE_ONE(sum);
-    #elif defined(__riscv_v_intrinsic)
+    #elif defined(__riscv_v)
                        // todo: RVV implementation
                        const int np = 0;
    #else
@@ -11742,7 +11778,7 @@ static void ggml_compute_forward_rwkv_wkv7_f32(
    int64_t h_stride_2d = head_size * head_size;

    #if defined(GGML_SIMD)
-        #if defined(__ARM_FEATURE_SVE) || defined(__riscv_v_intrinsic)
+        #if defined(__ARM_FEATURE_SVE) || defined(__riscv_v)
            // scalar Route to scalar implementation       //TODO: Write SVE code and RVV code
            for (int64_t t = 0; t < T; t++) {
                int64_t t_offset = t * t_stride;
@@ -3,6 +3,7 @@

 #include "ggml-cpu-impl.h"
 #include "simd-mappings.h"
+#include "vec.h"
 #include "ggml-quants.h"
 #include "quants.h"

@@ -62,6 +63,10 @@ void quantize_row_nvfp4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, i
    quantize_row_nvfp4_ref(x, y, k);
 }

+void quantize_row_f8_e4m3(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k) {
+    quantize_row_f8_e4m3_ref(x, y, k);
+}
+
 //
 // 2-6 bit quantization in super-blocks
 //
@@ -362,6 +367,49 @@ void ggml_vec_dot_nvfp4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
    *s = sumf;
 }

+void ggml_vec_dot_f8_e4m3_f32(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+
+    const ggml_fp8_e4m3_t * GGML_RESTRICT x = vx;
+    const float * GGML_RESTRICT y = vy;
+
+    float sumf = 0.0f;
+    int pos = 0;
+#if defined(GGML_SIMD) && !defined(__ARM_FEATURE_SVE) && !defined(__riscv_v_intrinsic)
+    GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO };
+    const int np = n - n % GGML_F32_STEP;
+    for (; pos < np; pos += GGML_F32_STEP) {
+        for (int j = 0; j < GGML_F32_ARR; ++j) {
+            const int offset = pos + j*GGML_F32_EPR;
+            GGML_F32_VEC ax;
+#if defined(__AVX512F__) && defined(__AVX512BW__)
+            const __m128i codes = _mm_loadu_si128((const __m128i *) (x + offset));
+            ax = _mm512_i32gather_ps(_mm512_cvtepu8_epi32(codes), ggml_table_f32_f8_e4m3, sizeof(float));
+#elif defined(__AVX2__) && !defined(__AVX512F__)
+            const __m128i codes = _mm_loadl_epi64((const __m128i *) (x + offset));
+            ax = _mm256_i32gather_ps(ggml_table_f32_f8_e4m3, _mm256_cvtepu8_epi32(codes), sizeof(float));
+#else
+            float values[GGML_F32_EPR];
+            for (int k = 0; k < GGML_F32_EPR; ++k) {
+                values[k] = ggml_table_f32_f8_e4m3[x[offset + k].bits];
+            }
+            ax = GGML_F32_VEC_LOAD(values);
+#endif
+            sum[j] = GGML_F32_VEC_FMA(sum[j], ax, GGML_F32_VEC_LOAD(y + offset));
+        }
+    }
+    GGML_F32_VEC_REDUCE(sumf, sum);
+#endif
+    for (; pos < n; ++pos) {
+        sumf += ggml_table_f32_f8_e4m3[x[pos].bits] * y[pos];
+    }
+    *s = sumf;
+}
+
 void ggml_vec_dot_q5_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
    const int qk = QK8_0;
    const int nb = n / qk;
@@ -23,6 +23,7 @@ void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in

 void quantize_row_mxfp4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_nvfp4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_f8_e4m3(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);

 void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
@@ -40,6 +41,7 @@ void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y,
 // Dot product
 void ggml_vec_dot_q1_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q2_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_f8_e4m3_f32(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
@@ -5,7 +5,7 @@
 #include "simd-mappings.h"

 // TODO: add support for sizeless vector types
-#if defined(GGML_SIMD) && !defined(__ARM_FEATURE_SVE) && !defined(__riscv_v_intrinsic)
+#if defined(GGML_SIMD) && !defined(__ARM_FEATURE_SVE) && !defined(__riscv_v)

 // TODO: untested on avx512
 // These are in units of GGML_F32_EPR
@@ -149,7 +149,7 @@ static void simd_gemm(
        C += N;
    }
 }
-#elif defined(GGML_SIMD) && defined(__riscv_v_intrinsic)
+#elif defined(GGML_SIMD) && defined(__riscv_v)
 // RM accumulators + 1 B vector = RM + 1 <= 8  =>  RM <= 7
 // Microkernel: C[RM x vl] += A[RM x K] * B[K x N]
 template <int RM>
@@ -14,7 +14,9 @@
 #include <arm_neon.h>
 #endif

-#if defined(__riscv_v_intrinsic)
+// __riscv_v_intrinsic reports compiler API support and can be defined without
+// enabling V; use the ISA feature macro to select RVV code.
+#if defined(__riscv_v)
 #include <riscv_vector.h>
 #endif

@@ -127,6 +129,8 @@ extern float ggml_table_f32_e8m0_half[1 << 8];
 // defined in ggml-cpu.c, initialized in ggml_cpu_init()
 extern float ggml_table_f32_ue4m3[1 << 8];

+extern float ggml_table_f32_f8_e4m3[1 << 8];
+
 // Use lookup table for E8M0 on x86 (faster than bit manipulation)
 #if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
 #define GGML_CPU_E8M0_TO_FP32_HALF(x) ggml_table_f32_e8m0_half[(uint8_t)(x)]
@@ -1304,7 +1308,7 @@ static inline void __lzs_f16cx4_store(ggml_fp16_t * x, float32x4_t v_y) {
 #define GGML_BF16_FMA_HI(acc, x, y) \
    (acc) = GGML_F32x4_FMA((acc), GGML_BF16_TO_F32_HI(x), GGML_BF16_TO_F32_HI(y))

-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)

 // compatible with vlen >= 128

@@ -1046,6 +1046,10 @@ class tensor_traits_common : public tensor_traits_base {
                        return true;
                }
            case GGML_OP_FLASH_ATTN_EXT:
+                // K/V row lists are handled by the generic CPU path
+                if (op->src[5] != nullptr) {
+                    return false;
+                }
                forward_flash_attn_ext_f16(params, op);
                return true;
            case GGML_OP_CONT:
@@ -1489,6 +1493,9 @@ static const ggml_backend_buffer_i ggml_backend_riscv64_spacemit_buffer_i = {
    /* .cpy_tensor      = */ nullptr,
    /* .clear           = */ ggml_backend_riscv64_spacemit_buffer_clear,
    /* .reset           = */ nullptr,
+    /* .set_tensor_n    = */ NULL,
+    /* .get_tensor_n    = */ NULL,
+    /* .cpy_tensor_n    = */ NULL,
 };

 static const char * ggml_backend_cpu_riscv64_spacemit_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
@@ -84,7 +84,7 @@ void ggml_vec_dot_f32(int n, float * GGML_RESTRICT s, size_t bs, const float * G
        }
        // reduce sum1,sum2 to sum1
        GGML_F32_VEC_REDUCE(sumf, sum1, sum2, sum3, sum4, sum5, sum6, sum7, sum8);
-    #elif defined(__riscv_v_intrinsic)
+    #elif defined(__riscv_v)
        int vl = __riscv_vsetvlmax_e32m8();
        vfloat32m1_t vs = __riscv_vfmv_v_f_f32m1(0.0f, 1);
        vfloat32m8_t vsum;
@@ -195,7 +195,7 @@ void ggml_vec_dot_bf16(int n, float * GGML_RESTRICT s, size_t bs, ggml_bf16_t *
    sumf += (ggml_float)_mm_cvtss_f32(g);

 #undef LOAD
-#elif defined(__riscv_v_intrinsic) && defined(__riscv_zvfbfwma)
+#elif defined(__riscv_v) && defined(__riscv_zvfbfwma)
    size_t vl = __riscv_vsetvlmax_e32m4();

    // initialize accumulators to all zeroes
@@ -318,7 +318,7 @@ void ggml_vec_dot_f16(int n, float * GGML_RESTRICT s, size_t bs, ggml_fp16_t * G
        sum1_hi = svadd_f32_m(DEFAULT_PG32, sum1_hi, sum3_hi);

        sumf = ggml_sve_sum_f32x2(sum1_lo, sum1_hi);
-    #elif defined(__riscv_v_intrinsic)
+    #elif defined(__riscv_v)
        #if defined(__riscv_zvfh)
            int vl = __riscv_vsetvlmax_e32m2();
            vfloat32m1_t vs = __riscv_vfmv_v_f_f32m1(0.0f, 1);
@@ -405,7 +405,7 @@ void ggml_vec_silu_f32(const int n, float * y, const float * x) {
    for (; i + 3 < n; i += 4) {
        vec_xst(ggml_v_silu(vec_xl(0, x + i)), 0, y + i);
    }
-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)
    for (int vl; i < n; i += vl) {
        vl = __riscv_vsetvl_e32m2(n - i);
        vfloat32m2_t vx = __riscv_vle32_v_f32m2(&x[i], vl);
@@ -446,7 +446,7 @@ void ggml_vec_swiglu_f32(const int n, float * y, const float * x, const float *
    for (; i + 3 < n; i += 4) {
        vec_xst(vec_mul(ggml_v_silu(vec_xl(0, x + i)), vec_xl(0, g + i)), 0, y + i);
    }
-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)
    for (int vl; i < n; i += vl) {
        vl = __riscv_vsetvl_e32m2(n - i);
        vfloat32m2_t vx = __riscv_vle32_v_f32m2(&x[i], vl);
@@ -602,7 +602,7 @@ ggml_float ggml_vec_cvar_f32(const int n, float * y, const float * x, const floa
        val = vec_mul(val, val);
        sum += (ggml_float)vec_hsum_f32x4(val);
    }
-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)
    vfloat64m1_t vsum = __riscv_vfmv_v_f_f64m1(0, 1);
    for (int vl; i < n; i += vl) {
        vl = __riscv_vsetvl_e32m2(n - i);
@@ -681,7 +681,7 @@ ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, float
        vec_xst(v_val, 0, y + i);
        sum += (ggml_float)vec_hsum_f32x4(v_val);
    }
-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)
    vfloat64m1_t vsum = __riscv_vfmv_v_f_f64m1(0, 1);
    for (int avl; i < n; i += avl) {
        avl = __riscv_vsetvl_e32m2(n - i);
@@ -206,7 +206,7 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * GG
        sumf[0] = ggml_sve_sum_f32x2(sum_0_lo, sum_0_hi);
        sumf[1] = ggml_sve_sum_f32x2(sum_1_lo, sum_1_hi);
        np = n;
-    #elif defined(__riscv_v_intrinsic)
+    #elif defined(__riscv_v)
        #if defined(__riscv_zvfh)
            size_t vl = __riscv_vsetvlmax_e32m4();

@@ -396,7 +396,7 @@ inline static void ggml_vec_mad_f32(const int n, float * GGML_RESTRICT y, const

            svst1_f32(pg, y + np2, ay1);
        }
-    #elif defined(__riscv_v_intrinsic)
+    #elif defined(__riscv_v)
        for (int i = 0, avl; i < n; i += avl) {
            avl = __riscv_vsetvl_e32m8(n - i);
            vfloat32m8_t ax = __riscv_vle32_v_f32m8(&x[i], avl);
@@ -513,7 +513,7 @@ inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * GGML_RESTRICT y,
        svst1_f16(pg, (__fp16 *)(y + np2), hy);
    }
    np = n;
-#elif defined(__riscv_v_intrinsic) // implies __riscv_v_intrinsic
+#elif defined(__riscv_v)
    #if defined (__riscv_zvfh)
        const ggml_fp16_t s = GGML_CPU_FP32_TO_FP16(v);
        const _Float16 scale = *(const _Float16*)(&s);
@@ -599,7 +599,7 @@ inline static void ggml_vec_mad_f32_unroll(const int n, const int xs, const int
                y[i] += x[k][i]*v[k][0];
            }
        }
-    #elif defined(__riscv_v_intrinsic)
+    #elif defined(__riscv_v)
        for (int i = 0, avl; i < n; i += avl) {
            avl = __riscv_vsetvl_e32m8(n - i);
            vfloat32m8_t ay = __riscv_vle32_v_f32m8(&y[i], avl);
@@ -660,7 +660,7 @@ inline static void ggml_vec_mad1_f32(const int n, float * y, const float * x, co
        for (int i = 0; i < n; ++i) {
            y[i] = x[i]*s + b;
        }
-    #elif defined(__riscv_v_intrinsic)
+    #elif defined(__riscv_v)
        for (int i = 0, avl; i < n; i += avl) {
            avl = __riscv_vsetvl_e32m8(n - i);
            vfloat32m8_t ax = __riscv_vle32_v_f32m8(&x[i], avl);
@@ -729,7 +729,7 @@ inline static void ggml_vec_scale_f32(const int n, float * y, const float   v) {
            ay1 = svmul_f32_m(pg, ay1, vx);
            svst1_f32(pg, y + i, ay1);
        }
-    #elif defined(__riscv_v_intrinsic)
+    #elif defined(__riscv_v)
        for (int i = 0, avl; i < n; i += avl) {
            avl = __riscv_vsetvl_e32m8(n - i);
            vfloat32m8_t ay = __riscv_vle32_v_f32m8(&y[i], avl);
@@ -793,7 +793,7 @@ inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float
        svst1_f16(pg, (__fp16 *)(y + np), out);
    }
    np = n;
-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)
    #if defined(__riscv_zvfh)
        const ggml_fp16_t s = GGML_CPU_FP32_TO_FP16(v);
        const _Float16 scale = *(const _Float16*)(&s);
@@ -1411,7 +1411,7 @@ inline static float32x4_t ggml_v_gelu_quick(float32x4_t x) {
    return vec_div(x, vec_add(one, exp_cx));
 }

-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)

 // adapted from arm limited optimized routine
 // the maximum error is 1.45358 plus 0.5 ulps
@@ -1469,7 +1469,7 @@ inline static vfloat32m2_t ggml_v_gelu_quick_m2(vfloat32m2_t x, int vl) {
    return __riscv_vfdiv_vv_f32m2(x, __riscv_vfadd_vf_f32m2(exp_cx, 1.0f, vl), vl);
 }

-#endif // __ARM_NEON / __AVX2__ / __SSE2__ / __VXE__ / __riscv_v_intrinsic
+#endif // __ARM_NEON / __AVX2__ / __SSE2__ / __VXE__ / __riscv_v

 inline static void ggml_vec_silu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
    for (int i = 0; i < n; ++i) {
@@ -0,0 +1,198 @@
+#include "batch-get.cuh"
+#include "common.cuh"
+#include "ggml-cpp.h"
+
+#include <climits>
+
+#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+struct ggml_cuda_batch_get_descriptor {
+    const unsigned char * src;
+    unsigned char * dst;
+    size_t size;
+};
+
+static __global__ void ggml_cuda_batch_get_kernel(const ggml_cuda_batch_get_descriptor * copies, size_t count) {
+    // Blocks along y cooperate on each descriptor.
+    const size_t index = blockIdx.x;
+    if (index >= count) {
+        return;
+    }
+    const size_t first = (size_t) blockIdx.y * blockDim.x + threadIdx.x;
+    const size_t stride = (size_t) gridDim.y * blockDim.x;
+    const auto copy = copies[index];
+    // Use 16-, 8-, 4-, or 2-byte copies when both pointers meet that alignment; threads copy separate chunks.
+    const uintptr_t alignment = (uintptr_t) copy.src | (uintptr_t) copy.dst;
+    size_t tail = 0;
+    if (alignment % 16 == 0) {
+        for (size_t i = first; i < copy.size / 16; i += stride) {
+            ggml_cuda_memcpy_1<16>(copy.dst + 16*i, copy.src + 16*i);
+        }
+        tail = copy.size / 16 * 16;
+    } else if (alignment % 8 == 0) {
+        for (size_t i = first; i < copy.size / 8; i += stride) {
+            ggml_cuda_memcpy_1<8>(copy.dst + 8*i, copy.src + 8*i);
+        }
+        tail = copy.size / 8 * 8;
+    } else if (alignment % 4 == 0) {
+        for (size_t i = first; i < copy.size / 4; i += stride) {
+            ggml_cuda_memcpy_1<4>(copy.dst + 4*i, copy.src + 4*i);
+        }
+        tail = copy.size / 4 * 4;
+    } else if (alignment % 2 == 0) {
+        for (size_t i = first; i < copy.size / 2; i += stride) {
+            ggml_cuda_memcpy_1<2>(copy.dst + 2*i, copy.src + 2*i);
+        }
+        tail = copy.size / 2 * 2;
+    }
+    // Copy trailing bytes, or the entire range when no wider alignment is available.
+    for (size_t i = tail + first; i < copy.size; i += stride) {
+        ggml_cuda_memcpy_1<1>(copy.dst + i, copy.src + i);
+    }
+}
+
+struct ggml_cuda_batch_get_mapping {
+    void * host = nullptr;
+    unsigned char * device = nullptr;
+
+    ggml_cuda_batch_get_mapping(void * host, size_t size) {
+        const cudaError_t err = cudaHostRegister(host, size, cudaHostRegisterMapped | cudaHostRegisterPortable);
+        if (err != cudaSuccess) {
+            (void) cudaGetLastError();
+            GGML_LOG_DEBUG("%s: failed to register %.2f MiB of pinned memory: %s\n", __func__,
+                           size / 1024.0 / 1024.0, cudaGetErrorString(err));
+            return;
+        }
+        this->host = host;
+        if (cudaHostGetDevicePointer((void **) &device, host, 0) != cudaSuccess) {
+            (void) cudaGetLastError();
+            device = nullptr;
+            return;
+        }
+    }
+
+    ggml_cuda_batch_get_mapping(const ggml_cuda_batch_get_mapping &) = delete;
+    ggml_cuda_batch_get_mapping & operator=(const ggml_cuda_batch_get_mapping &) = delete;
+
+    ~ggml_cuda_batch_get_mapping() {
+        if (host) {
+            // Release only the registration acquired by this object.
+            CUDA_CHECK(cudaHostUnregister(host));
+        }
+    }
+};
+#endif
+
+bool ggml_cuda_get_tensor_n(int device,
+        const ggml_tensor * const * tensors, void * const * data, const size_t * offsets,
+        const size_t * sizes, size_t n_tensors) {
+#if defined(GGML_USE_HIP) || defined(GGML_USE_MUSA)
+    GGML_UNUSED(device);
+    GGML_UNUSED(tensors);
+    GGML_UNUSED(data);
+    GGML_UNUSED(offsets);
+    GGML_UNUSED(sizes);
+    GGML_UNUSED(n_tensors);
+    return false;
+#else
+    // Provisional dispatch values based on experiments.
+    constexpr size_t min_batch_reads = 128;
+    constexpr size_t max_gather_read_bytes = 128 * 1024;
+    // Collect non-empty CPU destination ranges.
+    std::vector<std::pair<uintptr_t, uintptr_t>> ranges;
+    ranges.reserve(n_tensors);
+    size_t payload_bytes = 0;
+    // Number of non-empty reads whose size is at most max_gather_read_bytes.
+    size_t n_reads_small = 0;
+    for (size_t i = 0; i < n_tensors; ++i) {
+        if (sizes[i] == 0) {
+            continue;
+        }
+        const uintptr_t address = (uintptr_t) data[i];
+        GGML_ASSERT(address <= UINTPTR_MAX - sizes[i]);
+        GGML_ASSERT(payload_bytes <= SIZE_MAX - sizes[i]);
+        payload_bytes += sizes[i];
+        n_reads_small += sizes[i] <= max_gather_read_bytes;
+        ranges.emplace_back(address, address + sizes[i]);
+    }
+    if (ranges.empty()) {
+        return true;
+    }
+    // Total number of non-empty reads.
+    const size_t n_reads = ranges.size();
+    if (n_reads > SIZE_MAX / sizeof(ggml_cuda_batch_get_descriptor) || n_reads > INT_MAX) {
+        return false;
+    }
+    if (n_reads < 2 || (n_reads < min_batch_reads && n_reads_small == n_reads)) {
+        return false;
+    }
+    std::sort(ranges.begin(), ranges.end());
+    for (size_t i = 1; i < ranges.size(); ++i) {
+        // Preserve the ordered copy path when CPU destinations overlap.
+        if (ranges[i].first < ranges[i - 1].second) {
+            return false;
+        }
+    }
+    // Find the CPU span to register, including gaps between destinations.
+    const uintptr_t begin = ranges.front().first;
+    const uintptr_t end = ranges.back().second;
+    if (end - begin - payload_bytes > payload_bytes) {
+        return false;
+    }
+    // Register and map the existing CPU destination so the kernel can write to it directly.
+    ggml_cuda_batch_get_mapping mapping((void *) begin, end - begin);
+    if (!mapping.host) {
+        return false;
+    }
+    // Reads selected for gather; zero means all reads use DMA.
+    size_t n_reads_gather = mapping.device && n_reads_small >= min_batch_reads ? n_reads_small : 0;
+    // Keep descriptor storage alive until the final stream synchronization.
+    constexpr size_t max_batch_descriptors = 128 * 1024;
+    const size_t bytes = std::min(n_reads_gather, max_batch_descriptors) * sizeof(ggml_cuda_batch_get_descriptor);
+    ggml_backend_buffer_ptr descriptors(n_reads_gather ? ggml_backend_buft_alloc_buffer(ggml_backend_cuda_buffer_type(device), bytes) : nullptr);
+    if (!descriptors) {
+        n_reads_gather = 0;
+    }
+    const auto host_type = ggml_backend_cuda_host_buffer_type();
+    ggml_backend_buffer_ptr host_descriptors(n_reads_gather ? ggml_backend_buft_alloc_buffer(host_type, bytes) : nullptr);
+    if (!host_descriptors || ggml_backend_buffer_get_type(host_descriptors.get()) != host_type) {
+        n_reads_gather = 0;
+    }
+    if (n_reads_gather) {
+        auto * copies = static_cast<ggml_cuda_batch_get_descriptor *>(ggml_backend_buffer_get_base(host_descriptors.get()));
+        auto * device_copies = static_cast<ggml_cuda_batch_get_descriptor *>(ggml_backend_buffer_get_base(descriptors.get()));
+        const int threads = 256;
+        const size_t nsm = ggml_cuda_info().devices[device].nsm;
+        size_t i = 0;
+        for (size_t first = 0; first < n_reads_gather; first += max_batch_descriptors) {
+            const size_t n_batch = std::min(n_reads_gather - first, max_batch_descriptors);
+            size_t count = 0;
+            for (; i < n_tensors && count < n_batch; ++i) {
+                if (sizes[i] == 0 || sizes[i] > max_gather_read_bytes) {
+                    continue;
+                }
+                copies[count++] = {(const unsigned char *) tensors[i]->data + offsets[i],
+                        mapping.device + ((uintptr_t) data[i] - begin), sizes[i]};
+            }
+            const size_t blocks_per_copy = std::min<size_t>(UINT16_MAX, std::max<size_t>(1, (nsm + n_batch - 1) / n_batch));
+            const dim3 blocks((unsigned int) n_batch, (unsigned int) blocks_per_copy);
+            // Stream ordering keeps each kernel ahead of the next descriptor overwrite.
+            CUDA_CHECK(cudaMemcpyAsync(device_copies, copies, n_batch * sizeof(copies[0]), cudaMemcpyHostToDevice, cudaStreamPerThread));
+            ggml_cuda_batch_get_kernel<<<blocks, threads, 0, cudaStreamPerThread>>>(device_copies, n_batch);
+            CUDA_CHECK(cudaGetLastError());
+            if (first + n_batch < n_reads_gather) {
+                // Wait before refilling the pinned host descriptor buffer.
+                CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
+            }
+        }
+    }
+    // Keep the destination pinned for DMA too; overlapping destinations already declined above.
+    for (size_t i = 0; i < n_tensors; ++i) {
+        if (sizes[i] && (!n_reads_gather || sizes[i] > max_gather_read_bytes)) {
+            CUDA_CHECK(cudaMemcpyAsync(data[i], (const char *) tensors[i]->data + offsets[i], sizes[i], cudaMemcpyDeviceToHost, cudaStreamPerThread));
+        }
+    }
+    // Wait for GPU writes before releasing the CPU mapping and descriptor buffers.
+    CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
+    return true;
+#endif
+}
@@ -0,0 +1,7 @@
+#pragma once
+
+#include "ggml-backend.h"
+
+bool ggml_cuda_get_tensor_n(int device,
+        const ggml_tensor * const * tensors, void * const * data, const size_t * offsets,
+        const size_t * sizes, size_t n_tensors);
@@ -82,6 +82,7 @@
 #define GGML_CUDA_CC_RDNA2      (GGML_CUDA_CC_OFFSET_AMD + 0x1030) // RX 6000, minimum for dp4a
 #define GGML_CUDA_CC_RDNA3      (GGML_CUDA_CC_OFFSET_AMD + 0x1100) // RX 7000, minimum for WMMA
 #define GGML_CUDA_CC_RDNA3_5    (GGML_CUDA_CC_OFFSET_AMD + 0x1150) // AI 370, AI Max 395 laptops.
+#define GGML_CUDA_CC_RDNA4_M    (GGML_CUDA_CC_OFFSET_AMD + 0x1170) // gfx117X
 #define GGML_CUDA_CC_RDNA4      (GGML_CUDA_CC_OFFSET_AMD + 0x1200) // RX 9000

 #define GGML_CUDA_CC_IS_AMD(cc)     (cc >= GGML_CUDA_CC_OFFSET_AMD)
@@ -89,8 +90,9 @@
 #define GGML_CUDA_CC_IS_RDNA1(cc)   (cc >= GGML_CUDA_CC_RDNA1 && cc < GGML_CUDA_CC_RDNA2)
 #define GGML_CUDA_CC_IS_RDNA2(cc)   (cc >= GGML_CUDA_CC_RDNA2 && cc < GGML_CUDA_CC_RDNA3)
 #define GGML_CUDA_CC_IS_RDNA3_0(cc) (cc >= GGML_CUDA_CC_RDNA3 && cc < GGML_CUDA_CC_RDNA3_5)
-#define GGML_CUDA_CC_IS_RDNA3_5(cc) (cc >= GGML_CUDA_CC_RDNA3_5 && cc < GGML_CUDA_CC_RDNA4)
+#define GGML_CUDA_CC_IS_RDNA3_5(cc) (cc >= GGML_CUDA_CC_RDNA3_5 && cc < GGML_CUDA_CC_RDNA4_M)
 #define GGML_CUDA_CC_IS_RDNA3(cc)   (GGML_CUDA_CC_IS_RDNA3_0(cc) || GGML_CUDA_CC_IS_RDNA3_5(cc))
+#define GGML_CUDA_CC_IS_RDNA4_M(cc) (cc >= GGML_CUDA_CC_RDNA4_M && cc < GGML_CUDA_CC_RDNA4)
 #define GGML_CUDA_CC_IS_RDNA4(cc)   (cc >= GGML_CUDA_CC_RDNA4)
 #define GGML_CUDA_CC_IS_GCN_APU(cc) ((cc) == GGML_CUDA_CC_GFX909 || (cc) == GGML_CUDA_CC_GFX90C)
 #define GGML_CUDA_CC_IS_GCN(cc)     ((cc > GGML_CUDA_CC_OFFSET_AMD && cc < GGML_CUDA_CC_CDNA1) || GGML_CUDA_CC_IS_GCN_APU(cc))
@@ -277,9 +279,17 @@ static const char * cu_get_error_str(CUresult err) {
 #define AMD_MFMA_AVAILABLE
 #endif // defined(GGML_USE_HIP) && defined(CDNA) && !defined(GGML_HIP_NO_MMQ_MFMA)

-#if defined(GGML_USE_HIP) && (defined(RDNA4) || defined(RDNA3))
+#if defined(GGML_USE_HIP) && (defined(RDNA4) || defined(RDNA4_M))
+#define AMD_WMMA_128B_AVAILABLE
+#endif // defined(GGML_USE_HIP) && (defined(RDNA4) || defined(RDNA4_M))
+
+#if defined(GGML_USE_HIP) && defined(RDNA3)
+#define AMD_WMMA_256B_AVAILABLE
+#endif // defined(GGML_USE_HIP) && defined(RDNA3)
+
+#if defined(AMD_WMMA_128B_AVAILABLE) || defined(AMD_WMMA_256B_AVAILABLE)
 #define AMD_WMMA_AVAILABLE
-#endif // defined(GGML_USE_HIP) && defined(RDNA4)
+#endif // defined(AMD_WMMA_128B_AVAILABLE) || defined(AMD_WMMA_256B_AVAILABLE)

 // The Volta instructions are in principle available on Turing or newer but they are effectively unusable:
 #if !defined(GGML_USE_HIP) && __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
@@ -326,7 +336,7 @@ static bool fast_fp16_hardware_available(const int cc) {
 // To be used for feature selection of external libraries, e.g. cuBLAS.
 static bool fp16_mma_hardware_available(const int cc) {
    return (GGML_CUDA_CC_IS_NVIDIA(cc) && cc >= GGML_CUDA_CC_VOLTA) ||
-        GGML_CUDA_CC_IS_CDNA(cc) || GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4(cc) ||
+        GGML_CUDA_CC_IS_CDNA(cc) || GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4_M(cc) || GGML_CUDA_CC_IS_RDNA4(cc) ||
        (GGML_CUDA_CC_IS_MTHREADS(cc) && cc >= GGML_CUDA_CC_QY2);
 }

@@ -355,7 +365,7 @@ static bool amd_mfma_available(const int cc) {
 }

 static bool amd_wmma_available(const int cc) {
-    return (GGML_CUDA_CC_IS_RDNA4(cc) || GGML_CUDA_CC_IS_RDNA3(cc));
+    return GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4_M(cc) || GGML_CUDA_CC_IS_RDNA4(cc);
 }

 static bool volta_mma_available(const int cc) {
@@ -713,7 +723,7 @@ static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, i
 #if defined(GGML_USE_HIP)
 #if defined(CDNA) || defined(RDNA2) || defined(__gfx906__)
    c = __builtin_amdgcn_sdot4(a, b, c, false);
-#elif defined(RDNA3) || defined(RDNA4)
+#elif defined(RDNA3) || defined(RDNA4_M) || defined(RDNA4)
    c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
 #elif defined(RDNA1) || defined(__gfx900__)
    int tmp1;
@@ -758,9 +768,9 @@ static __device__ __forceinline__ void ggml_cuda_mad(float & acc, const float2 v
    acc += v.y*u.y;
 }

-#if defined(GGML_USE_HIP) && (defined(RDNA2) || defined(RDNA3) || defined(RDNA4) || defined(__gfx906__) || defined(CDNA))
+#if defined(GGML_USE_HIP) && (defined(RDNA2) || defined(RDNA3) || defined(RDNA4_M) || defined(RDNA4) || defined(__gfx906__) || defined(CDNA))
 #define V_DOT2_F32_F16_AVAILABLE
-#endif // defined(GGML_USE_HIP) && (defined(RDNA2) || defined(RDNA3) || defined(RDNA4) || defined(__gfx906__) || defined(CDNA))
+#endif // defined(GGML_USE_HIP) && (defined(RDNA2) || defined(RDNA3) || defined(RDNA4_M) || defined(RDNA4) || defined(__gfx906__) || defined(CDNA))

 static __device__ __forceinline__ void ggml_cuda_mad(float & acc, const half2 v, const half2 u) {
 #ifdef V_DOT2_F32_F16_AVAILABLE
@@ -1459,16 +1469,21 @@ struct ggml_backend_cuda_context {
    // when the computation is split across CPU/GPU (e.g., with --n-cpu-moe)
    std::unordered_map<const void *, std::unique_ptr<ggml_cuda_graph>> cuda_graphs;

-    int64_t last_graph_eviction_sweep = 0;
+    int64_t next_graph_eviction_time = 0;
+    int64_t last_graph_use_time = 0;

-    ggml_cuda_graph * cuda_graph(const void * first_node_ptr) {
-        const int64_t time_now = ggml_time_us();
+    ggml_cuda_graph * cuda_graph(const void * first_node_ptr, int64_t time_now = ggml_time_us()) {
+        // Give all cached graphs 10s to be reused after an idle period, without updating each graph.
+        if (!cuda_graphs.empty() && time_now - last_graph_use_time >= 10'000'000) {
+            next_graph_eviction_time = time_now + 10'000'000;
+        }
+        last_graph_use_time = time_now;

-        // sweep every 5s, evicting cuda graphs unused for >=10s
-        if (time_now - last_graph_eviction_sweep >= 5'000'000) {
-            last_graph_eviction_sweep = time_now;
+        // Sweep every 5s during active use, evicting graphs unused for at least 10s.
+        if (time_now >= next_graph_eviction_time) {
+            next_graph_eviction_time = time_now + 5'000'000;
            for (auto it = cuda_graphs.begin(); it != cuda_graphs.end(); ) {
-                if (time_now - it->second->last_used_time >= 10'000'000) {
+                if (it->first != first_node_ptr && time_now - it->second->last_used_time >= 10'000'000) {
                    it = cuda_graphs.erase(it);
                } else {
                    ++it;
@@ -25,6 +25,7 @@ typedef void (* fattn_kernel_t)(
        const char * __restrict__ mask,
        const char * __restrict__ sinks,
        const int  * __restrict__ KV_max,
+        const int  * __restrict__ KV_rows,
        float      * __restrict__ dst,
        float2     * __restrict__ dst_meta,
        const float scale,
@@ -991,8 +992,13 @@ void launch_fattn(

    const bool V_is_K_view = V->view_src && (V->view_src == K || (V->view_src == K->view_src && V->view_offs == K->view_offs));

-    const ggml_tensor * mask  = dst->src[3];
-    const ggml_tensor * sinks = dst->src[4];
+    const ggml_tensor * mask    = dst->src[3];
+    const ggml_tensor * sinks   = dst->src[4];
+    const ggml_tensor * kv_rows = dst->src[5];
+
+    // with kv_rows the kernel iterates over the mask columns and reads K/V rows through kv_rows
+    const int64_t n_kv_cols = kv_rows ? kv_rows->ne[0] : K->ne[1];
+    GGML_ASSERT(!kv_rows || !use_sparse);

    ggml_tensor * KQV = dst;

@@ -1113,8 +1119,8 @@ void launch_fattn(

    // Optional optimization where the mask is scanned to determine whether part of the calculation can be skipped.
    // Only worth the overhead if there is at lease one FATTN_KQ_STRIDE x FATTN_KQ_STRIDE square to be skipped or
-    //     multiple sequences of possibly different lengths.
-    const bool scan_mask = !use_sparse && mask && K->ne[1] % FATTN_KQ_STRIDE == 0 && (Q->ne[1] >= 1024 || Q->ne[3] > 1);
+    //     multiple sequences of possibly different lengths, or kv_rows lists that end with padding.
+    const bool scan_mask = !use_sparse && mask && n_kv_cols % FATTN_KQ_STRIDE == 0 && (Q->ne[1] >= 1024 || Q->ne[3] > 1 || kv_rows);
    if (scan_mask) {
        const int64_t s31 = mask->nb[1] / sizeof(half2);
        const int64_t s33 = mask->nb[3] / sizeof(half2);
@@ -1123,7 +1129,7 @@ void launch_fattn(
        const dim3 block_dim_KV_max(FATTN_KQ_STRIDE/2, 1, 1);

        const int ne_KV_max = blocks_num_KV_max.x*blocks_num_KV_max.y;
-        const int iter_k = K->ne[1] / FATTN_KQ_STRIDE;
+        const int iter_k = n_kv_cols / FATTN_KQ_STRIDE;

        KV_max.alloc(ne_KV_max);
        ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(blocks_num_KV_max, block_dim_KV_max, 0, main_stream);
@@ -1132,13 +1138,19 @@ void launch_fattn(
        CUDA_CHECK(cudaGetLastError());
    }

+    // K/V are shared by all slices when read through kv_rows
+    if (kv_rows) {
+        nb13 = 0;
+        nb23 = 0;
+    }
+
    const dim3 block_dim(warp_size, nwarps, 1);
    int max_blocks_per_sm = 1; // Max. number of active blocks limited by occupancy.
    CUDA_CHECK(cudaOccupancyMaxActiveBlocksPerMultiprocessor(&max_blocks_per_sm, fattn_kernel, block_dim.x * block_dim.y * block_dim.z, nbytes_shared));
    GGML_ASSERT(max_blocks_per_sm > 0);
    int parallel_blocks = max_blocks_per_sm;

-    const int64_t n_kv = use_sparse ? n_kv_max : K->ne[1];
+    const int64_t n_kv = use_sparse ? n_kv_max : n_kv_cols;
    const int ntiles_KV = (n_kv + nbatch_fa - 1) / nbatch_fa; // Max. number of parallel blocks limited by KV cache length.

    dim3 blocks_num;
@@ -1255,6 +1267,7 @@ void launch_fattn(
        mask ? ((const char *) mask->data) : nullptr,
        sinks ? ((const char *) sinks->data) : nullptr,
        KV_max.ptr,
+        kv_rows ? (const int *) kv_rows->data : nullptr,
        !stream_k && parallel_blocks > 1 ? dst_tmp.ptr : (float *) KQV->data, dst_tmp_meta.ptr,
        scale, max_bias, m0, m1, n_head_log2, logit_softcap,
        Q->ne[0], ne01,     Q->ne[2], Q->ne[3], Q->nb[1], Q->nb[2], Q->nb[3],
@@ -420,6 +420,9 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_tile(
                    // padded slots gather row 0, the -inf mask removes their contribution
                    const int32_t index = i < i_sup ? indices[k_VKQ_0 + i] : 0;
                    i_KV = index >= 0 ? index : 0;
+                } else if (indices) {
+                    // K/V rows given per mask column, negative rows are padding
+                    i_KV = max(indices[k_VKQ_0 + i], 0);
                } else {
                    i_KV = k_VKQ_0 + i;
                }
@@ -467,8 +470,11 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_tile(
                    if constexpr (use_sparse) {
                        const int32_t index = i < i_sup ? indices[k_VKQ_0 + i] : -1;
                        src = index >= 0 ? KV + int64_t(index)*stride_KV + k*h2_per_chunk : zero;
+                    } else if (!oob_check || i < i_sup) {
+                        const int64_t i_KV = indices ? max(indices[k_VKQ_0 + i], 0) : k_VKQ_0 + i;
+                        src = KV + i_KV*stride_KV + k*h2_per_chunk;
                    } else {
-                        src = !oob_check || i < i_sup ? KV + int64_t(k_VKQ_0 + i)*stride_KV + k*h2_per_chunk : zero;
+                        src = zero;
                    }
                    ggml_cuda_memcpy_1<16>(swizzle<stride_tile>(tile_KV, i*stride_tile + k*h2_per_chunk, i), src);
                }
@@ -634,7 +640,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
        cp_async_wait_all();
        __syncthreads();
        flash_attn_ext_f16_load_tile<stride_tile_V, nwarps, nbatch_fa, use_cp_async, oob_check, use_sparse>
-            (V_h2, tile_V, nbatch_V2, stride_V, k_VKQ_0, k_VKQ_sup, nullptr);
+            (V_h2, tile_V, nbatch_V2, stride_V, k_VKQ_0, k_VKQ_sup, indices);
    } else {
        // the sparse mask values are gathered per element, always load them synchronously
        constexpr bool use_cp_async = nstages == 1 && !use_sparse;
@@ -991,7 +997,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
                    (mask_h, tile_mask, stride_mask, k_VKQ_0 + nbatch_fa, k_VKQ_sup, jt*ncols1, ne01, nullptr);
            }
            flash_attn_ext_f16_load_tile<stride_tile_K, nwarps, nbatch_fa, use_cp_async, oob_check, use_sparse>
-                (K_h2, tile_K, nbatch_K2, stride_K, k_VKQ_0 + nbatch_fa, k_VKQ_sup, nullptr);
+                (K_h2, tile_K, nbatch_K2, stride_K, k_VKQ_0 + nbatch_fa, k_VKQ_sup, indices);
        }
    }

@@ -1255,7 +1261,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
    T_B_KQ    Q_B[(Q_in_reg ? DKQ/(2*T_B_KQ::J) : 1)];
 #if defined(TURING_MMA_AVAILABLE)
    T_C_VKQ VKQ_C[cols_per_warp == 8 ? DV/T_C_VKQ::I : DV/(2*T_C_VKQ::J)];
-#elif defined(AMD_WMMA_AVAILABLE) && defined(RDNA3)
+#elif defined(AMD_WMMA_256B_AVAILABLE)
    T_C_VKQ VKQ_C[DV % 32 != 0       ? DV/T_C_VKQ::J : DV/(2*T_C_VKQ::J)];
 #elif defined(AMD_MFMA_AVAILABLE)
    T_C_VKQ VKQ_C[                                     DV/T_C_VKQ::J];
@@ -1343,7 +1349,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
                (mask_h, tile_mask, stride_mask, kb0*nbatch_fa, k_VKQ_sup, jt*ncols1, ne01, nullptr);
        }
        flash_attn_ext_f16_load_tile<stride_tile_K, nwarps, nbatch_fa, use_cp_async, oob_check, use_sparse>
-            (K_h2, tile_K, nbatch_K2, stride_K, kb0*nbatch_fa, k_VKQ_sup, nullptr);
+            (K_h2, tile_K, nbatch_K2, stride_K, kb0*nbatch_fa, k_VKQ_sup, indices);
    }

    // kb0_start is always < kb0_stop so the last iter can be executed unconditionally.
@@ -1802,6 +1808,7 @@ static __global__ void flash_attn_ext_f16(
        const char * mask_ptr,
        const char * sinks_ptr,
        const int  * KV_max_ptr,
+        const int  * KV_rows_ptr,
        float      * dst_ptr,
        float2     * dst_meta_ptr,
        const float scale,
@@ -1866,7 +1873,7 @@ static __global__ void flash_attn_ext_f16(
 #endif // defined(AMD_WMMA_AVAILABLE)

 #if defined(AMD_MFMA_AVAILABLE)
-    if (ncols1*ncols2 < 16 || (DKQ > 256 && ncols1*ncols2 < 32)) {
+    if (ncols1*ncols2 < 16 || (DKQ > 256 && ncols1*ncols2 < 32 && !use_sparse)) {
        NO_DEVICE_CODE;
        return;
    }
@@ -1924,7 +1931,8 @@ static __global__ void flash_attn_ext_f16(

        const half2 * V_h2 = V_is_K_view ? K_h2 : (const half2 *) (V + nb23*sequence + nb22*z_KV);
        const float * sinks_f = sinks ? (const float *) sinks + zt_Q : nullptr;
-        const int32_t * indices = use_sparse ? sparse_indices + (int64_t(sequence % ne33)*iter_j + jt)*ne11 : nullptr;
+        const int32_t * indices = use_sparse ? sparse_indices + (int64_t(sequence % ne33)*iter_j + jt)*ne11 :
+                                  KV_rows_ptr ? KV_rows_ptr + int64_t(sequence)*ne11 : nullptr;

        const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, zt_Q, n_head_log2, m0, m1) : 1.0f;

@@ -1973,7 +1981,8 @@ static __global__ void flash_attn_ext_f16(

    const half2 * V_h2 = V_is_K_view ? K_h2 : (const half2 *) (V + nb23*sequence + nb22*z_KV);
    const float * sinks_f = sinks ? (const float *) sinks + zt_Q : nullptr;
-    const int32_t * indices = use_sparse ? sparse_indices + (int64_t(sequence % ne33)*iter_j + jt)*ne11 : nullptr;
+    const int32_t * indices = use_sparse ? sparse_indices + (int64_t(sequence % ne33)*iter_j + jt)*ne11 :
+                              KV_rows_ptr ? KV_rows_ptr + int64_t(sequence)*ne11 : nullptr;

    const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, zt_Q, n_head_log2, m0, m1) : 1.0f;

@@ -1989,7 +1998,7 @@ static __global__ void flash_attn_ext_f16(
        (Q_f2, K_h2, V_h2, mask_h, indices, sinks_f, dstk, dst_meta, scale, slope, logit_softcap,
         ne01, ne02, gqa_ratio, ne11, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, zt_gqa, kb0_start, kb0_stop);
 #else
-    GGML_UNUSED_VARS(Q_ptr, K_ptr, V_ptr, mask_ptr, sinks_ptr, KV_max_ptr, dst_ptr, dst_meta_ptr, scale,
+    GGML_UNUSED_VARS(Q_ptr, K_ptr, V_ptr, mask_ptr, sinks_ptr, KV_max_ptr, KV_rows_ptr, dst_ptr, dst_meta_ptr, scale,
        max_bias, m0, m1, n_head_log2, logit_softcap,
        ne00, ne01, ne02, ne03,
              nb01, nb02, nb03,
@@ -2054,7 +2063,7 @@ void ggml_cuda_flash_attn_ext_mma_f16_case(ggml_backend_cuda_context & ctx, ggml
    bool use_sparse = false;
    if (logit_softcap == 0.0f) {
        constexpr bool use_logit_softcap = false;
-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+#if !defined(GGML_USE_MUSA)
        if constexpr (ggml_cuda_flash_attn_ext_mma_f16_may_use_sparse(DKQ, DV, ncols1, ncols2)) {
            if (ggml_cuda_flash_attn_ext_mma_f16_shall_use_sparse(cc, dst, ncols1, ncols2)) {
                constexpr bool use_sparse_kernel = true;
@@ -2077,7 +2086,7 @@ void ggml_cuda_flash_attn_ext_mma_f16_case(ggml_backend_cuda_context & ctx, ggml
                }
            }
        } else
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+#endif // !defined(GGML_USE_MUSA)
        {
            constexpr bool use_sparse_kernel = false;
            fattn_kernel = flash_attn_ext_f16<DKQ, DV, ncols1, ncols2, use_logit_softcap, V_is_K_view, use_sparse_kernel>;
@@ -797,6 +797,7 @@ static __global__ void flash_attn_tile(
        const char * mask_ptr,
        const char * sinks_ptr,
        const int  * KV_max_ptr,
+        const int  * KV_rows_ptr,
        float      * dst_ptr,
        float2     * dst_meta_ptr,
        const float scale,
@@ -821,6 +822,7 @@ static __global__ void flash_attn_tile(
    const int  * GGML_CUDA_RESTRICT KV_max   = KV_max_ptr;
    float      * GGML_CUDA_RESTRICT dst      = dst_ptr;
    float2     * GGML_CUDA_RESTRICT dst_meta = dst_meta_ptr;
+    GGML_UNUSED(KV_rows_ptr);

    // Skip unused kernel variants for faster compilation:

@@ -1132,7 +1134,7 @@ static __global__ void flash_attn_tile(
        }
    }
 #else
-    GGML_UNUSED_VARS(Q_ptr, K_ptr, V_ptr, mask_ptr, sinks_ptr, KV_max_ptr, dst_ptr, dst_meta_ptr, scale,
+    GGML_UNUSED_VARS(Q_ptr, K_ptr, V_ptr, mask_ptr, sinks_ptr, KV_max_ptr, KV_rows_ptr, dst_ptr, dst_meta_ptr, scale,
        max_bias, m0, m1, n_head_log2, logit_softcap,
        ne00, ne01, ne02, ne03,
              nb01, nb02, nb03,
@@ -25,6 +25,7 @@ static __global__ void flash_attn_ext_vec(
        const char * mask_ptr,
        const char * sinks_ptr,
        const int  * KV_max_ptr,
+        const int  * KV_rows_ptr,
        float      * dst_ptr,
        float2     * dst_meta_ptr,
        const float scale,
@@ -50,6 +51,7 @@ static __global__ void flash_attn_ext_vec(
    const int  * GGML_CUDA_RESTRICT KV_max   = KV_max_ptr;
    float      * GGML_CUDA_RESTRICT dst      = dst_ptr;
    float2     * GGML_CUDA_RESTRICT dst_meta = dst_meta_ptr;
+    GGML_UNUSED(KV_rows_ptr);

    // Skip unused kernel variants for faster compilation:
    if (use_logit_softcap && !(D == 128 || D == 256)) {
@@ -514,7 +516,7 @@ static __global__ void flash_attn_ext_vec(
        dst_meta[((sequence*int(ne01.z) + ic0 + tid)*ne02 + head)*gridDim.y + blockIdx.y] = make_float2(KQ_max[tid], KQ_sum[tid]);
    }
 #else
-    GGML_UNUSED_VARS(Q_ptr, K_ptr, V_ptr, mask_ptr, sinks_ptr, KV_max_ptr, dst_ptr, dst_meta_ptr, scale,
+    GGML_UNUSED_VARS(Q_ptr, K_ptr, V_ptr, mask_ptr, sinks_ptr, KV_max_ptr, KV_rows_ptr, dst_ptr, dst_meta_ptr, scale,
        max_bias, m0, m1, n_head_log2, logit_softcap,
        ne00, ne01, ne02, ne03,
              nb01, nb02, nb03,
@@ -5,7 +5,8 @@
 #include "fattn-vec.cuh"
 #include "fattn.cuh"

-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+#if !defined(GGML_USE_MUSA)
+
 // one list per group of ncols1 queries: a column is selected if any query of the group can see it
 template <int ncols1, bool oob>
 __launch_bounds__(256, 1)
@@ -14,10 +15,20 @@ static __global__ void flash_attn_mask_to_sparse_indices(
        const int n_kv_max, const int64_t s31, const int64_t s33) {
    ggml_cuda_pdl_sync();

+    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
    constexpr int values_per_lane = 8;
+    using lane_mask_t = std::conditional_t<warp_size == 64, uint64_t, uint32_t>;
+    constexpr uint64_t ballot_mask = ~(lane_mask_t)0;
+    const auto popc = [](const lane_mask_t mask) {
+        if constexpr (warp_size == 64) {
+            return __popcll(mask);
+        } else {
+            return __popc(mask);
+        }
+    };
    const int tid      = threadIdx.x;
-    const int warp     = tid / WARP_SIZE;
-    const int lane     = tid % WARP_SIZE;
+    const int warp     = tid / warp_size;
+    const int lane     = tid % warp_size;
    const int sequence = blockIdx.y;
    const int group    = blockIdx.x;

@@ -27,7 +38,7 @@ static __global__ void flash_attn_mask_to_sparse_indices(
    const half * mask = mask_ptr + sequence*s33 + q0*s31;
    int32_t * indices = indices_ptr + (int64_t(sequence)*gridDim.x + group)*n_kv_max;

-    __shared__ int warp_offsets[256/WARP_SIZE];
+    __shared__ int warp_offsets[256/warp_size];
    __shared__ int row_count;
    __shared__ int chunk_count;

@@ -37,11 +48,11 @@ static __global__ void flash_attn_mask_to_sparse_indices(
    __syncthreads();

    for (int i0 = 0; i0 < ne30; i0 += blockDim.x*values_per_lane) {
-        uint32_t selected_warp[values_per_lane];
+        lane_mask_t selected_warp[values_per_lane];
        int warp_count = 0;
 #pragma unroll
        for (int item = 0; item < values_per_lane; ++item) {
-            const int i = i0 + (warp*values_per_lane + item)*WARP_SIZE + lane;
+            const int i = i0 + (warp*values_per_lane + item)*warp_size + lane;
            bool selected = false;
            if (i < ne30) {
 #pragma unroll
@@ -49,8 +60,8 @@ static __global__ void flash_attn_mask_to_sparse_indices(
                    selected |= (!oob || q < q1 - q0) && isfinite(__half2float(mask[q*s31 + i]));
                }
            }
-            selected_warp[item] = __ballot_sync(0xFFFFFFFF, selected);
-            warp_count += __popc(selected_warp[item]);
+            selected_warp[item] = (lane_mask_t)__ballot_sync(ballot_mask, (int) selected);
+            warp_count += popc(selected_warp[item]);
        }

        if (lane == 0) {
@@ -61,7 +72,7 @@ static __global__ void flash_attn_mask_to_sparse_indices(
        if (tid == 0) {
            int offset = 0;
 #pragma unroll
-            for (int iw = 0; iw < 256/WARP_SIZE; ++iw) {
+            for (int iw = 0; iw < 256/warp_size; ++iw) {
                const int count = warp_offsets[iw];
                warp_offsets[iw] = offset;
                offset += count;
@@ -70,16 +81,16 @@ static __global__ void flash_attn_mask_to_sparse_indices(
        }
        __syncthreads();

-        const uint32_t lane_mask = lane == 0 ? 0 : (1u << lane) - 1;
+        const lane_mask_t lane_mask = lane == 0 ? 0 : ((lane_mask_t)1 << lane) - 1;
        int warp_item_offset = 0;
 #pragma unroll
        for (int item = 0; item < values_per_lane; ++item) {
-            const int i = i0 + (warp*values_per_lane + item)*WARP_SIZE + lane;
-            const int dst = row_count + warp_offsets[warp] + warp_item_offset + __popc(selected_warp[item] & lane_mask);
-            if ((selected_warp[item] & (uint32_t(1) << lane)) && dst < n_kv_max) {
+            const int i = i0 + (warp*values_per_lane + item)*warp_size + lane;
+            const int dst = row_count + warp_offsets[warp] + warp_item_offset + popc(selected_warp[item] & lane_mask);
+            if ((selected_warp[item] & ((lane_mask_t)1 << lane)) && dst < n_kv_max) {
                indices[dst] = i;
            }
-            warp_item_offset += __popc(selected_warp[item]);
+            warp_item_offset += popc(selected_warp[item]);
        }
        __syncthreads();

@@ -101,13 +112,13 @@ static __global__ void flash_attn_mask_to_sparse_indices(
    // the dependent grid reads indices, signal once the row is complete
    ggml_cuda_pdl_lc();
 }
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+#endif // !defined(GGML_USE_MUSA)

 void ggml_cuda_flash_attn_ext_compact_mask(
        const ggml_tensor * mask, int32_t * indices, int32_t * counts, int32_t n_queries, int32_t ncols1, int32_t n_kv_max, cudaStream_t stream) {
-#if defined(GGML_USE_HIP) || defined(GGML_USE_MUSA)
+#ifdef GGML_USE_MUSA
    GGML_UNUSED_VARS(mask, indices, counts, n_queries, ncols1, n_kv_max, stream);
-    GGML_ABORT("sparse flash attention is only supported on NVIDIA CUDA");
+    GGML_ABORT("sparse flash attention is not supported on MUSA");
 #else
    const int64_t s31 = mask->nb[1] / sizeof(half);
    const int64_t s33 = mask->nb[3] / sizeof(half);
@@ -122,16 +133,17 @@ void ggml_cuda_flash_attn_ext_compact_mask(
    ggml_cuda_kernel_launch(kernel, launch_params,
        (const half *) mask->data, indices, counts, int(mask->ne[0]), n_queries, n_kv_max, s31, s33);
    CUDA_CHECK(cudaGetLastError());
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+#endif // !defined(GGML_USE_MUSA)
 }

 bool ggml_cuda_flash_attn_ext_mma_f16_shall_use_sparse(const int cc, const ggml_tensor * dst, const int ncols1, const int ncols2) {
-#if defined(GGML_USE_HIP) || defined(GGML_USE_MUSA)
+#ifdef GGML_USE_MUSA
    GGML_UNUSED_VARS(cc, dst, ncols1, ncols2);
    return false;
 #else
    const ggml_tensor * Q    = dst->src[0];
    const ggml_tensor * K    = dst->src[1];
+    const ggml_tensor * V    = dst->src[2];
    const ggml_tensor * mask = dst->src[3];

    float max_bias = 0.0f;
@@ -139,16 +151,21 @@ bool ggml_cuda_flash_attn_ext_mma_f16_shall_use_sparse(const int cc, const ggml_
    memcpy(&max_bias,      (const float *) dst->op_params + 1, sizeof(float));
    memcpy(&logit_softcap, (const float *) dst->op_params + 2, sizeof(float));

+    // On CDNA only the DKQ=576 kernel variant with ncols2=16 is compiled with sparse support.
+    if (amd_mfma_available(cc) && !ggml_cuda_flash_attn_ext_mma_f16_may_use_sparse(int(Q->ne[0]), int(V->ne[0]), 1, 16)) {
+        return false;
+    }
+
    const int32_t n_kv_max = ggml_get_op_params_i32(dst, 4);

    // the dense kernel handles up to 64/ncols2 queries per K/V pass, the single-query gather has to beat that
    const int64_t n_gather = (ncols1 == 1 ? std::min<int64_t>(Q->ne[1], 64/ncols2) : ncols1) * (int64_t) n_kv_max;

-    return GGML_CUDA_CC_IS_NVIDIA(cc) && turing_mma_available(cc) &&
+    return ((GGML_CUDA_CC_IS_NVIDIA(cc) && turing_mma_available(cc)) || amd_mfma_available(cc)) && dst->src[5] == nullptr &&
        mask != nullptr && n_kv_max > 0 && max_bias == 0.0f && logit_softcap == 0.0f &&
        mask->ne[0] == K->ne[1] && mask->ne[1] >= Q->ne[1] && mask->ne[2] == 1 &&
-        K->ne[1] >= std::max<int64_t>(4096, 2*n_gather);
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+        K->ne[1] >= std::max<int64_t>(4096, GGML_CUDA_CC_IS_NVIDIA(cc) ? 2*n_gather : 2LL*n_kv_max);
+#endif // !defined(GGML_USE_MUSA)
 }

 template <int DKQ, int DV, int ncols2>
@@ -156,7 +173,7 @@ static void ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1(ggml_backend_cuda_con
    const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
    const ggml_tensor * Q = dst->src[0];

-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+#if !defined(GGML_USE_MUSA)
    if constexpr (ggml_cuda_flash_attn_ext_mma_f16_may_use_sparse(DKQ, DV, 1, ncols2)) {
        // a sparse variant at the full tile width gathers the union of its queries once, prefer it for large batches
        constexpr bool has_wide_sparse = ggml_cuda_flash_attn_ext_mma_f16_may_use_sparse(DKQ, DV, 64/ncols2, ncols2);
@@ -165,7 +182,7 @@ static void ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1(ggml_backend_cuda_con
            return;
        }
    }
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+#endif // !defined(GGML_USE_MUSA)

    if constexpr (ncols2 <= 8) {
        if (turing_mma_available(cc) && Q->ne[1] <= 8/ncols2) {
@@ -199,12 +216,15 @@ static void ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2(ggml_backend_cuda_con
    const ggml_tensor * V    = dst->src[2];
    const ggml_tensor * mask = dst->src[3];

+    // with kv_rows the kernel iterates over the mask columns
+    const int64_t n_kv = dst->src[5] ? dst->src[5]->ne[0] : K->ne[1];
+
    float max_bias = 0.0f;
    memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float));

    // Edge cases like no mask, ALiBi, unpadded K/V, or misaligned addresses for large data transfers
    //     are put into the template specialization without GQA optimizations.
-    bool use_gqa_opt = mask && max_bias == 0.0f && K->ne[1] % FATTN_KQ_STRIDE == 0;
+    bool use_gqa_opt = mask && max_bias == 0.0f && n_kv % FATTN_KQ_STRIDE == 0;
    for (const ggml_tensor * t : {Q, K, V, mask}) {
        if (t == nullptr || ggml_is_quantized(t->type)) {
            continue;
@@ -365,7 +385,9 @@ static void ggml_cuda_flash_attn_ext_mma_f16(ggml_backend_cuda_context & ctx, gg

            GGML_ASSERT(Q->ne[2] % K->ne[2] == 0);
            const int gqa_ratio = Q->ne[2] / K->ne[2];
-            if (gqa_ratio == 20 && GGML_CUDA_CC_IS_NVIDIA(cc)) { // GLM 4.7 Flash
+            if (amd_mfma_available(cc) && ggml_cuda_flash_attn_ext_mma_f16_shall_use_sparse(cc, dst, 1, 16)) {
+                ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 16>(ctx, dst);
+            } else if (gqa_ratio == 20 && GGML_CUDA_CC_IS_NVIDIA(cc)) { // GLM 4.7 Flash
                if (cc >= GGML_CUDA_CC_DGX_SPARK) {
                    if (Q->ne[1] <= 8) {
                        ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 16>(ctx, dst);
@@ -548,6 +570,10 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
    const ggml_tensor * K     = dst->src[1];
    const ggml_tensor * V     = dst->src[2];
    const ggml_tensor * mask  = dst->src[3];
+    const ggml_tensor * kv_rows = dst->src[5];
+
+    // with kv_rows the kernel iterates over the mask columns
+    const int64_t n_kv = kv_rows ? kv_rows->ne[0] : K->ne[1];

    const int gqa_ratio = Q->ne[2] / K->ne[2];
    GGML_ASSERT(Q->ne[2] % K->ne[2] == 0);
@@ -557,7 +583,7 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const

    // The effective batch size for the kernel can be increased by gqa_ratio.
    // The kernel versions without this optimization are also used for ALiBi, if there is no mask, or if the KV cache is not padded,
-    bool gqa_opt_applies = gqa_ratio >= 2 && mask && max_bias == 0.0f && K->ne[1] % FATTN_KQ_STRIDE == 0;
+    bool gqa_opt_applies = gqa_ratio >= 2 && mask && max_bias == 0.0f && n_kv % FATTN_KQ_STRIDE == 0;
    for (const ggml_tensor * t : {Q, K, V, mask}) {
        if (t == nullptr || ggml_is_quantized(t->type)) {
            continue;
@@ -629,6 +655,12 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
        return BEST_FATTN_KERNEL_NONE;
    }

+    // only the MMA kernel reads K/V through kv_rows
+    if (kv_rows) {
+        return GGML_CUDA_CC_IS_NVIDIA(cc) && turing_mma_available(cc) && mask && Q->ne[0] != 40 && Q->ne[0] != 72 ?
+            BEST_FATTN_KERNEL_MMA_F16 : BEST_FATTN_KERNEL_NONE;
+    }
+
    // For small batch sizes the vector kernel may be preferable over the kernels optimized for large batch sizes:
    // 192 satisfies % 64 == 0 but has no vec instance (DKQ != DV); force it onto the MMA path.
    const bool can_use_vector_kernel = Q->ne[0] <= 256 && Q->ne[0] % 64 == 0 && Q->ne[0] != 192 && K->ne[1] % FATTN_KQ_STRIDE == 0;
@@ -679,6 +711,11 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
        return BEST_FATTN_KERNEL_MMA_F16;
    }

+    // On CDNA, the sparse MMA kernel is used for DKQ=576 regardless of the head size heuristics.
+    if (amd_mfma_available(cc) && ggml_cuda_flash_attn_ext_mma_f16_shall_use_sparse(cc, dst, 1, 16)) {
+        return BEST_FATTN_KERNEL_MMA_F16;
+    }
+
    // AMD MFMA needs a certain minimum batch size to outscale the tile kernel for large head sizes.
    if (amd_mfma_available(cc) && Q->ne[0] != 40 && Q->ne[0] != 72) {
        if ((Q->ne[0] <= 64 && Q->ne[1] * gqa_ratio_eff > 8)) {
@@ -9,6 +9,7 @@
 #include "ggml-cuda/arange.cuh"
 #include "ggml-cuda/argmax.cuh"
 #include "ggml-cuda/argsort.cuh"
+#include "ggml-cuda/batch-get.cuh"
 #include "ggml-cuda/binbcast.cuh"
 #include "ggml-cuda/clamp.cuh"
 #include "ggml-cuda/col2im-1d.cuh"
@@ -861,6 +862,51 @@ static void ggml_backend_cuda_buffer_get_tensor_2d(ggml_backend_buffer_t buffer,
    CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
 }

+static void ggml_backend_cuda_buffer_set_tensor_n(ggml_backend_buffer_t buffer, struct ggml_tensor * const * tensors, const void * const * data, const size_t * offsets, const size_t * sizes, size_t n_tensors) {
+    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
+
+    ggml_cuda_set_device(ctx->device);
+
+    for (size_t i = 0; i < n_tensors; i++) {
+        if (sizes[i] == 0) {
+            continue;
+        }
+        CUDA_CHECK(cudaMemcpyAsync(
+            (char *) tensors[i]->data + offsets[i],
+            data[i],
+            sizes[i],
+            cudaMemcpyHostToDevice,
+            cudaStreamPerThread));
+    }
+
+    CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
+}
+
+static void ggml_backend_cuda_buffer_get_tensor_n(ggml_backend_buffer_t buffer, const struct ggml_tensor * const * tensors, void * const * data, const size_t * offsets, const size_t * sizes, size_t n_tensors) {
+    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
+
+    ggml_cuda_set_device(ctx->device);
+
+    if (!ggml_cuda_pinned_memory_disabled() &&
+            ggml_cuda_get_tensor_n(ctx->device, tensors, data, offsets, sizes, n_tensors)) {
+        return;
+    }
+
+    for (size_t i = 0; i < n_tensors; i++) {
+        if (sizes[i] == 0) {
+            continue;
+        }
+        CUDA_CHECK(cudaMemcpyAsync(
+            data[i],
+            (const char *) tensors[i]->data + offsets[i],
+            sizes[i],
+            cudaMemcpyDeviceToHost,
+            cudaStreamPerThread));
+    }
+
+    CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
+}
+
 static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
    if (ggml_backend_buffer_is_cuda(src->buffer)) {
        ggml_backend_cuda_buffer_context * src_ctx = (ggml_backend_cuda_buffer_context *)src->buffer->context;
@@ -886,6 +932,54 @@ static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, co
    GGML_UNUSED(buffer);
 }

+static bool ggml_backend_cuda_buffer_cpy_tensor_n(ggml_backend_buffer_t buffer, const struct ggml_tensor * const * srcs, struct ggml_tensor * const * dsts, size_t n_tensors) {
+    ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
+
+    for (size_t i = 0; i < n_tensors; i++) {
+        ggml_backend_buffer_t src_buf = srcs[i]->view_src ? srcs[i]->view_src->buffer : srcs[i]->buffer;
+        if (!ggml_backend_buffer_is_cuda(src_buf)) {
+            return false;
+        }
+    }
+
+#ifdef GGML_CUDA_NO_PEER_COPY
+    for (size_t i = 0; i < n_tensors; i++) {
+        ggml_backend_buffer_t src_buf = srcs[i]->view_src ? srcs[i]->view_src->buffer : srcs[i]->buffer;
+        ggml_backend_buffer_t dst_buf = dsts[i]->view_src ? dsts[i]->view_src->buffer : dsts[i]->buffer;
+        ggml_backend_cuda_buffer_context * src_ctx = (ggml_backend_cuda_buffer_context *)src_buf->context;
+        ggml_backend_cuda_buffer_context * dst_ctx_i = (ggml_backend_cuda_buffer_context *)dst_buf->context;
+        const int src_physical = ggml_cuda_get_physical_device(src_ctx->device);
+        const int dst_physical = ggml_cuda_get_physical_device(dst_ctx_i->device);
+        if (src_physical != dst_physical) {
+            return false;
+        }
+    }
+#endif
+
+    ggml_cuda_set_device(dst_ctx->device);
+
+    for (size_t i = 0; i < n_tensors; i++) {
+        ggml_backend_buffer_t src_buf = srcs[i]->view_src ? srcs[i]->view_src->buffer : srcs[i]->buffer;
+        ggml_backend_buffer_t dst_buf = dsts[i]->view_src ? dsts[i]->view_src->buffer : dsts[i]->buffer;
+        ggml_backend_cuda_buffer_context * src_ctx = (ggml_backend_cuda_buffer_context *)src_buf->context;
+        ggml_backend_cuda_buffer_context * dst_ctx_i = (ggml_backend_cuda_buffer_context *)dst_buf->context;
+        const int src_physical = ggml_cuda_get_physical_device(src_ctx->device);
+        const int dst_physical = ggml_cuda_get_physical_device(dst_ctx_i->device);
+        if (src_physical == dst_physical) {
+            CUDA_CHECK(cudaMemcpyAsync(dsts[i]->data, srcs[i]->data, ggml_nbytes(srcs[i]), cudaMemcpyDeviceToDevice, cudaStreamPerThread));
+        } else {
+#ifdef GGML_CUDA_NO_PEER_COPY
+            GGML_ABORT("peer copy not supported");
+#else
+            CUDA_CHECK(cudaMemcpyPeerAsync(dsts[i]->data, dst_physical, srcs[i]->data, src_physical, ggml_nbytes(srcs[i]), cudaStreamPerThread));
+#endif
+        }
+    }
+
+    CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
+    return true;
+}
+
 static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;

@@ -906,6 +1000,9 @@ static const ggml_backend_buffer_i ggml_backend_cuda_buffer_interface = {
    /* .cpy_tensor      = */ ggml_backend_cuda_buffer_cpy_tensor,
    /* .clear           = */ ggml_backend_cuda_buffer_clear,
    /* .reset           = */ NULL,
+    /* .set_tensor_n    = */ ggml_backend_cuda_buffer_set_tensor_n,
+    /* .get_tensor_n    = */ ggml_backend_cuda_buffer_get_tensor_n,
+    /* .cpy_tensor_n    = */ ggml_backend_cuda_buffer_cpy_tensor_n,
 };

 // cuda buffer type
@@ -1026,6 +1123,18 @@ struct ggml_backend_cuda_comm_context {

    ggml_cuda_ar_pipeline *     ar_pipeline = nullptr;

+    // Number of ranks in the communicator when each rank is a separate process. Zero unless
+    // comm_init_rank created this context.
+    int                         world_size = 0;
+
+    // Number of ranks taking part in the reduction. For a communicator from comm_init_rank this is
+    // world_size. Otherwise it is the number of backends this process holds. The NCCL reduction
+    // uses it to choose the tensor size below which it reduces in F32 rather than converting to
+    // BF16 first.
+    size_t n_ranks() const {
+        return world_size > 0 ? (size_t) world_size : backends.size();
+    }
+
 #ifdef GGML_USE_NCCL
    std::vector<ncclComm_t>     comms;
 #endif // GGML_USE_NCCL
@@ -1053,6 +1162,7 @@ static bool ggml_backend_cuda_comm_allreduce_nccl(
    }

    const size_t n_backends = comm_ctx->backends.size();
+    const size_t n_ranks    = comm_ctx->n_ranks();

    for (size_t i = 0; i < n_backends; ++i) {
        GGML_ASSERT(tensors[i] != nullptr);
@@ -1062,7 +1172,7 @@ static bool ggml_backend_cuda_comm_allreduce_nccl(

    // For small tensors, simply reduce them as FP32.
    // The following heuristic for how "small" a tensor should be is based on RTX 4090s connected via 16x PCIe 4.0.
-    if ((n_backends <= 2 && ne < 32768) || (n_backends == 3 && ne < 131072) || (n_backends >= 4 && ne < 262144)) {
+    if ((n_ranks <= 2 && ne < 32768) || (n_ranks == 3 && ne < 131072) || (n_ranks >= 4 && ne < 262144)) {
        for (size_t i = 0; i < n_backends; ++i) {
            if ((tensors[i]->flags & GGML_TENSOR_FLAG_COMPUTE) == 0) {
                ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) comm_ctx->backends[i]->context;
@@ -1293,6 +1403,90 @@ static void * ggml_backend_cuda_comm_init(ggml_backend_t * backends, size_t n_ba
    return ret;
 }

+#ifdef GGML_USE_NCCL
+// Builds the communicator with ncclCommInitRank, for the case where each rank is its own
+// process. Blocks until all world_size ranks have joined.
+static void * ggml_backend_cuda_comm_init_rank(ggml_backend_t backend, int rank, int world_size,
+                                               ggml_backend_comm_bootstrap_callback bootstrap_callback, void * bootstrap_callback_data) {
+    if (world_size < 1 || rank < 0 || rank >= world_size) {
+        GGML_LOG_ERROR("%s: invalid rank %d for world size %d\n", __func__, rank, world_size);
+        return nullptr;
+    }
+
+    // Every rank sends whether its local checks passed, and rank 0 also sends the NCCL unique id.
+    // So all ranks either join together or fail together, and no rank waits in ncclCommInitRank alone.
+    struct bootstrap_msg {
+        uint8_t      ok;
+        ncclUniqueId id;
+    };
+    bootstrap_msg local;
+    memset(&local, 0, sizeof(local));
+    local.ok = 1;
+
+    if (!ggml_backend_is_cuda(backend)) {
+        GGML_LOG_ERROR("%s: backend is not a CUDA backend\n", __func__);
+        local.ok = 0;
+    } else {
+        // NCCL requires one distinct physical GPU per rank, see ggml_backend_cuda_comm_init_nccl.
+        const ggml_cuda_device_info & info = ggml_cuda_info();
+        if (info.device_count > info.physical_device_count) {
+            GGML_LOG_ERROR("%s: CUDA virtual devices are in use, which NCCL does not support\n", __func__);
+            local.ok = 0;
+        }
+    }
+    if (local.ok && rank == 0) {
+        const ncclResult_t rc = ncclGetUniqueId(&local.id);
+        if (rc != ncclSuccess) {
+            GGML_LOG_ERROR("%s: ncclGetUniqueId failed (%s)\n", __func__, ncclGetErrorString(rc));
+            local.ok = 0;
+        }
+    }
+
+    std::vector<bootstrap_msg> all(world_size);
+    if (!bootstrap_callback(bootstrap_callback_data, &local, all.data(), sizeof(local))) {
+        GGML_LOG_ERROR("%s: bootstrap exchange failed on rank %d\n", __func__, rank);
+        return nullptr;
+    }
+    for (int i = 0; i < world_size; i++) {
+        if (!all[i].ok) {
+            GGML_LOG_ERROR("%s: rank %d failed its setup checks\n", __func__, i);
+            return nullptr;
+        }
+    }
+
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backend->context;
+
+    auto * ret = new ggml_backend_cuda_comm_context;
+    ret->backends.push_back(backend);
+    ret->dev_ids.push_back(cuda_ctx->device);
+    ret->world_size = world_size;
+    ret->comms.resize(1);
+    ret->try_allreduce = ggml_backend_cuda_comm_try_allreduce_nccl;
+
+    // On DGX Spark, NCCL picks 64 channels by default, which is slower and uses more memory than 8.
+    // NCCL_MAX_CTAS still overrides this.
+    ncclConfig_t config = NCCL_CONFIG_INITIALIZER;
+    const int cc = ggml_cuda_info().devices[cuda_ctx->device].cc;
+    if (GGML_CUDA_CC_IS_NVIDIA(cc) && cc == GGML_CUDA_CC_DGX_SPARK) {
+        config.maxCTAs = 8;
+    }
+
+    ggml_cuda_set_device(cuda_ctx->device);
+    const ncclResult_t rc = ncclCommInitRankConfig(&ret->comms[0], world_size, all[0].id, rank, &config);
+    if (rc != ncclSuccess) {
+        GGML_LOG_ERROR("%s: ncclCommInitRankConfig failed for rank %d/%d (%s)\n",
+                       __func__, rank, world_size, ncclGetErrorString(rc));
+        ret->comms.clear();
+        delete ret;
+        return nullptr;
+    }
+
+    GGML_LOG_INFO("%s: joined NCCL communicator as rank %d/%d on device %d\n",
+                  __func__, rank, world_size, cuda_ctx->device);
+    return ret;
+}
+#endif // GGML_USE_NCCL
+
 // Top-level dispatch -- calls the function pointer chosen by comm_init.
 // Returns false to let the meta-backend's butterfly run.
 static bool ggml_backend_cuda_comm_allreduce_tensor(void * comm_ctx_v, struct ggml_tensor ** tensors) {
@@ -1565,7 +1759,7 @@ static void ggml_cuda_mul_mat_cublas_impl(ggml_backend_cuda_context & ctx, const
    if (compute_type == GGML_TYPE_F16) {
        prefer_f32_output = cc == GGML_CUDA_CC_VOLTA || GGML_CUDA_CC_IS_RDNA4(cc) || GGML_CUDA_CC_IS_CDNA(cc);
    } else if (compute_type == GGML_TYPE_BF16) {
-        prefer_f32_output = !GGML_CUDA_CC_IS_RDNA3(cc) && !GGML_CUDA_CC_IS_CDNA(cc);
+        prefer_f32_output = !GGML_CUDA_CC_IS_RDNA3(cc) && !GGML_CUDA_CC_IS_RDNA4_M(cc) && !GGML_CUDA_CC_IS_CDNA(cc);
    }

    if (prefer_f32_output) {
@@ -6293,6 +6487,11 @@ static void * ggml_backend_cuda_reg_get_proc_address(ggml_backend_reg_t reg, con
    if (strcmp(name, "ggml_backend_comm_allreduce_tensor") == 0) {
        return (void *)ggml_backend_cuda_comm_allreduce_tensor;
    }
+#ifdef GGML_USE_NCCL
+    if (strcmp(name, "ggml_backend_comm_init_rank") == 0) {
+        return (void *)ggml_backend_cuda_comm_init_rank;
+    }
+#endif // GGML_USE_NCCL
    if (strcmp(name, "ggml_backend_register_host_buffer") == 0) {
        return (void *)ggml_backend_cuda_register_host_buffer;
    }
@@ -76,11 +76,11 @@ namespace ggml_cuda_mma {
        // For the A/C matrices this means I major == row major, J major == column major.
        // For the B matrix this means I major == column major, J major == row major.
        // MIRRORED == Each data value is held exactly once per thread subgroup.
-        DATA_LAYOUT_I_MAJOR           =  0, // Always used for Turing, Ampere, Ada Lovelace, consumer Blackwell, matrix A&B for RDNA4 and CDNA.
-        DATA_LAYOUT_J_MAJOR           = 10, // Matrix C for CDNA and RDNA4, int and float matrix C for RDNA3.
+        DATA_LAYOUT_I_MAJOR           =  0, // Always used for Turing, Ampere, Ada Lovelace, consumer Blackwell, matrix A&B for WMMA128 and CDNA.
+        DATA_LAYOUT_J_MAJOR           = 10, // Matrix C for CDNA and WMMA128, int and float matrix C for WMMA256.
        DATA_LAYOUT_I_MAJOR_MIRRORED  = 20, // Volta, matrix A&B for RDNA3.
        DATA_LAYOUT_J_MAJOR_MIRRORED  = 30,
-        DATA_LAYOUT_I_MAJOR_SCRAMBLED = 40, // Scrambled matrix C for faster transposition (RDNA4/CDNA), convert to float to unscramble.
+        DATA_LAYOUT_I_MAJOR_SCRAMBLED = 40, // Scrambled matrix C for faster transposition (WMMA128/CDNA), convert to float to unscramble.
    };
    // Implemented mma combinations are:
    //   - (I_MAJOR, I_MAJOR)          -> I_MAJOR
@@ -88,11 +88,11 @@ namespace ggml_cuda_mma {
    //   - (I_MAJOR, J_MAJOR_MIRRORED) -> I_MAJOR

    static constexpr __device__ data_layout get_input_data_layout() {
-#if defined(RDNA3) || defined(VOLTA_MMA_AVAILABLE)
+#if defined(AMD_WMMA_256B_AVAILABLE) || defined(VOLTA_MMA_AVAILABLE)
        return DATA_LAYOUT_I_MAJOR_MIRRORED;
 #else
        return DATA_LAYOUT_I_MAJOR;
-#endif // defined(RDNA3) || defined(VOLTA_MMA_AVAILABLE)
+#endif // defined(AMD_WMMA_256B_AVAILABLE) || defined(VOLTA_MMA_AVAILABLE)
    }

    template <int I_, int J_, typename T, data_layout ds_=DATA_LAYOUT_I_MAJOR>
@@ -201,7 +201,7 @@ namespace ggml_cuda_mma {

        static __device__ __forceinline__ int get_j(const int l) {
            if constexpr (I == 16 && J == 16) {
-#if defined(RDNA3)
+#if defined(AMD_WMMA_256B_AVAILABLE)
                if constexpr (std::is_same_v<T, float> || std::is_same_v<T, int>) {
                    // matrix C
                    return 2 * l + (threadIdx.x / 16);
@@ -210,11 +210,11 @@ namespace ggml_cuda_mma {
                    return l;
                }
 #else
-                // matrix C is the transposed matrix A&B on RDNA4
+                // matrix C is the transposed matrix A&B on WMMA128
                return ne * (threadIdx.x / 16) + l;
-#endif // defined(RDNA3)
+#endif // defined(AMD_WMMA_256B_AVAILABLE)
            } else if constexpr (I == 16 && J == 8) {
-                // mmq input for RDNA4
+                // mmq input for WMMA128
                return ne * (threadIdx.x / 16) + l;
            } else if constexpr (I == 16 && J == 4) {
                return ne * (threadIdx.x / 16) + l;
@@ -573,7 +573,7 @@ namespace ggml_cuda_mma {
        static constexpr int         I  = I_;
        static constexpr int         J  = J_;
        static constexpr data_layout dl = DATA_LAYOUT_I_MAJOR_MIRRORED;
-#if defined(RDNA3)
+#if defined(AMD_WMMA_256B_AVAILABLE)
        static constexpr int         ne = tile<I_, J_, float, DATA_LAYOUT_I_MAJOR_MIRRORED>::ne;

        half2 x[ne] = {{0.0f, 0.0f}};
@@ -616,7 +616,7 @@ namespace ggml_cuda_mma {
                return -1;
            }
        }
-#endif // defined(RDNA3)
+#endif // defined(AMD_WMMA_256B_AVAILABLE)
    };

    template <int I_, int J_>
@@ -694,7 +694,7 @@ namespace ggml_cuda_mma {
    };

    static __device__ __forceinline__ tile<16, 16, half2, DATA_LAYOUT_I_MAJOR> unscramble(const tile<16, 16, half2, DATA_LAYOUT_I_MAJOR_SCRAMBLED> & t) {
-#if defined(AMD_MFMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4))
+#if defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_128B_AVAILABLE)
        tile<16, 16, half2, DATA_LAYOUT_I_MAJOR> ret;
 #pragma unroll
        for (int l0 = 0; l0 < t.ne/2; ++l0) {
@@ -705,7 +705,7 @@ namespace ggml_cuda_mma {
 #else
        NO_DEVICE_CODE;
        GGML_UNUSED(t);
-#endif // defined(AMD_MFMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4))
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_128B_AVAILABLE)
    }

 #if defined(TURING_MMA_AVAILABLE)
@@ -726,7 +726,7 @@ namespace ggml_cuda_mma {

        return ret;
    }
-#elif defined(AMD_WMMA_AVAILABLE) && defined(RDNA3)
+#elif defined(AMD_WMMA_256B_AVAILABLE)
    static __device__ __forceinline__ tile<16, 8, half2, DATA_LAYOUT_I_MAJOR_MIRRORED> get_half2(
            const tile<16, 16, float, DATA_LAYOUT_I_MAJOR> & tile_float) {
        tile<16, 8, half2, DATA_LAYOUT_I_MAJOR_MIRRORED> ret;
@@ -1171,7 +1171,7 @@ namespace ggml_cuda_mma {
            : "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[3]));
 #endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
 #elif defined(AMD_WMMA_AVAILABLE)
-#if defined(RDNA4)
+#if defined(AMD_WMMA_128B_AVAILABLE)
        using halfx8_t = __attribute__((ext_vector_type(8))) _Float16;
        halfx8_t& acc_frag = reinterpret_cast<halfx8_t&>(D.x[0]);
        const halfx8_t& a_frag = reinterpret_cast<const halfx8_t&>(A.x[0]);
@@ -1180,7 +1180,7 @@ namespace ggml_cuda_mma {
 #else
        GGML_UNUSED_VARS(D, A, B);
        NO_DEVICE_CODE;
-#endif // defined(RDNA4)
+#endif // defined(AMD_WMMA_128B_AVAILABLE)
 #elif defined(AMD_MFMA_AVAILABLE)
        // MFMA: FP16 input, FP32 accumulate, convert back to half2.
        using halfx4_t = __attribute__((ext_vector_type(4))) _Float16;
@@ -1218,7 +1218,7 @@ namespace ggml_cuda_mma {
    static __device__ __forceinline__ void mma(
            tile<16, 16, half2, DATA_LAYOUT_I_MAJOR_SCRAMBLED> & D, const tile<32, 8, half2, DATA_LAYOUT_I_MAJOR> & A,
            const tile<16, 8, half2, DATA_LAYOUT_I_MAJOR> & B) {
-#if defined(AMD_MFMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4))
+#if defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_128B_AVAILABLE)
        tile<16, 8, half2>       * D16 = (tile<16, 8, half2>       *) &D;
        const tile<16, 8, half2> * A16 = (const tile<16, 8, half2> *) &A;
        mma(D16[0], A16[0], B);
@@ -1226,7 +1226,7 @@ namespace ggml_cuda_mma {
 #else
        GGML_UNUSED_VARS(D, A, B);
        NO_DEVICE_CODE;
-#endif // defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_128B_AVAILABLE)
    }

    template <data_layout dl_ab, data_layout dl_d>
@@ -1373,14 +1373,14 @@ namespace ggml_cuda_mma {
            : "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[3]));
 #endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
 #elif defined(AMD_WMMA_AVAILABLE)
-#if defined(RDNA4)
+#if defined(AMD_WMMA_128B_AVAILABLE)
        using halfx8_t = __attribute__((ext_vector_type(8))) _Float16;
        using floatx8_t = __attribute__((ext_vector_type(8))) float;
        floatx8_t& acc_frag = reinterpret_cast<floatx8_t&>(D.x[0]);
        const halfx8_t& a_frag = reinterpret_cast<const halfx8_t&>(A.x[0]);
        const halfx8_t& b_frag = reinterpret_cast<const halfx8_t&>(B.x[0]);
        acc_frag = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(a_frag, b_frag, acc_frag);
-#elif defined(RDNA3)
+#elif defined(AMD_WMMA_256B_AVAILABLE)
        using halfx16_t = __attribute__((ext_vector_type(16))) _Float16;
        using floatx8_t = __attribute__((ext_vector_type(8))) float;
        floatx8_t& acc_frag = reinterpret_cast<floatx8_t&>(D.x[0]);
@@ -1390,7 +1390,7 @@ namespace ggml_cuda_mma {
 #else
        GGML_UNUSED_VARS(D, A, B);
        NO_DEVICE_CODE;
-#endif // RDNA4
+#endif // AMD_WMMA_128B_AVAILABLE
 #elif defined(AMD_MFMA_AVAILABLE)
        using halfx4_t = __attribute__((ext_vector_type(4))) _Float16;
        using floatx4_t = __attribute__((ext_vector_type(4))) float;
@@ -1408,14 +1408,14 @@ namespace ggml_cuda_mma {
    static __device__ __forceinline__ void mma(
            tile<16, 16, float, dl_d> & D, const tile<16, 8, nv_bfloat162, dl_ab> & A, const tile<16, 8, nv_bfloat162, dl_ab> & B) {
 #if defined(AMD_WMMA_AVAILABLE)
-#if defined(RDNA4)
+#if defined(AMD_WMMA_128B_AVAILABLE)
        using bf16x8_t = __attribute__((ext_vector_type(8))) short;
        using floatx8_t = __attribute__((ext_vector_type(8))) float;
        floatx8_t& acc_frag = reinterpret_cast<floatx8_t&>(D.x[0]);
        const bf16x8_t& a_frag = reinterpret_cast<const bf16x8_t&>(A.x[0]);
        const bf16x8_t& b_frag = reinterpret_cast<const bf16x8_t&>(B.x[0]);
        acc_frag = __builtin_amdgcn_wmma_f32_16x16x16_bf16_w32_gfx12(a_frag, b_frag, acc_frag);
-#elif defined(RDNA3)
+#elif defined(AMD_WMMA_256B_AVAILABLE)
        using bf16x16_t = __attribute__((ext_vector_type(16))) short;
        using floatx8_t = __attribute__((ext_vector_type(8))) float;
        floatx8_t& acc_frag = reinterpret_cast<floatx8_t&>(D.x[0]);
@@ -1425,7 +1425,7 @@ namespace ggml_cuda_mma {
 #else
        GGML_UNUSED_VARS(D, A, B);
        NO_DEVICE_CODE;
-#endif // defined(RDNA4)
+#endif // defined(AMD_WMMA_128B_AVAILABLE)
 #elif defined(AMD_MFMA_AVAILABLE)
        using floatx4_t = __attribute__((ext_vector_type(4))) float;
        floatx4_t& acc_frag = reinterpret_cast<floatx4_t&>(D.x[0]);
@@ -1467,19 +1467,19 @@ namespace ggml_cuda_mma {
 #elif defined(AMD_WMMA_AVAILABLE)
        using int32x8_t = __attribute__((__vector_size__(8 * sizeof(int)))) int;
        int32x8_t * acc = (int32x8_t *) D.x;
-#if defined(RDNA4)
+#if defined(AMD_WMMA_128B_AVAILABLE)
        using int32x2_t = __attribute__((__vector_size__(2 * sizeof(int)))) int;
        int32x2_t * a_vec = (int32x2_t *) A.x;
        int32x2_t * b_vec = (int32x2_t *) B.x;
        acc[0] = __builtin_amdgcn_wmma_i32_16x16x16_iu8_w32_gfx12(true, a_vec[0], true, b_vec[0], acc[0], true);
        acc[0] = __builtin_amdgcn_wmma_i32_16x16x16_iu8_w32_gfx12(true, a_vec[1], true, b_vec[1], acc[0], true);
-#elif defined(RDNA3)
+#elif defined(AMD_WMMA_256B_AVAILABLE)
        using int32x4_t = __attribute__((__vector_size__(4 * sizeof(int)))) int;
        int32x4_t * a_vec = (int32x4_t *) A.x;
        int32x4_t * b_vec = (int32x4_t *) B.x;
        acc[0] = __builtin_amdgcn_wmma_i32_16x16x16_iu8_w32(true, a_vec[0], true, b_vec[0], acc[0], true);
        acc[0] = __builtin_amdgcn_wmma_i32_16x16x16_iu8_w32(true, a_vec[1], true, b_vec[1], acc[0], true);
-#endif // RDNA4
+#endif // AMD_WMMA_128B_AVAILABLE
 #else
        GGML_UNUSED_VARS(D, A, B);
        NO_DEVICE_CODE;
@@ -1556,7 +1556,7 @@ namespace ggml_cuda_mma {
    static __device__ __forceinline__ void mma(
            tile<16, 16, half2, DATA_LAYOUT_I_MAJOR> & D, const tile<32,  8, half2, DATA_LAYOUT_I_MAJOR_MIRRORED> & A,
            const tile<16,  8, half2, DATA_LAYOUT_I_MAJOR_MIRRORED> & B) {
-#if defined(AMD_WMMA_AVAILABLE) && defined(RDNA3)
+#if defined(AMD_WMMA_256B_AVAILABLE)
        using halfx16_t = __attribute__((ext_vector_type(16))) _Float16;
        halfx16_t       * xD = (halfx16_t       *) D.x;
        const halfx16_t * xA = (const halfx16_t *) A.x;
@@ -1585,17 +1585,17 @@ namespace ggml_cuda_mma {
 #elif defined(AMD_WMMA_AVAILABLE)
        using int32x8_t = __attribute__((__vector_size__(8 * sizeof(int)))) int;
        int32x8_t * acc = (int32x8_t *) D.x;
-#if defined(RDNA4)
+#if defined(AMD_WMMA_128B_AVAILABLE)
        using int32x2_t = __attribute__((__vector_size__(2 * sizeof(int)))) int;
        int32x2_t * a_vec = (int32x2_t *) A.x;
        int32x2_t * b_vec = (int32x2_t *) B.x;
        acc[0] = __builtin_amdgcn_wmma_i32_16x16x16_iu8_w32_gfx12(true, a_vec[0], true, b_vec[0], acc[0], false);
-#elif defined(RDNA3)
+#elif defined(AMD_WMMA_256B_AVAILABLE)
        using int32x4_t = __attribute__((__vector_size__(4 * sizeof(int)))) int;
        int32x4_t * a_vec = (int32x4_t *) A.x;
        int32x4_t * b_vec = (int32x4_t *) B.x;
        acc[0] = __builtin_amdgcn_wmma_i32_16x16x16_iu8_w32(true, a_vec[0], true, b_vec[0], acc[0], false);
-#endif // RDNA4
+#endif // AMD_WMMA_128B_AVAILABLE
 #else
        GGML_UNUSED(D);
        GGML_UNUSED(A);
@@ -242,7 +242,7 @@ void ggml_cuda_mul_mat_q(

            // Each expert only sees ne12*n_expert_used/ne02 tokens on average.
            // On RDNA3 and RDNA4 it is faster to pick the tile size against this value instead of ne12.
-            if (GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4(cc)) {
+            if (GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4_M(cc) || GGML_CUDA_CC_IS_RDNA4(cc)) {
                ncols_opt = (ne12*n_expert_used + ne02 - 1) / ne02;
            }
        }
@@ -470,7 +470,7 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11, int64_t
    }

    if (amd_wmma_available(cc)) {
-        if (GGML_CUDA_CC_IS_RDNA3(cc)) {
+        if (GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4_M(cc)) {
            // High expert counts are almost always better on MMQ due to
            //     the synchronization overhead in the cuBLAS/hipBLAS path:
            // https://github.com/ggml-org/llama.cpp/pull/18202
@@ -487,7 +487,7 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11, int64_t
                    return ne11 <= (GGML_CUDA_CC_IS_RDNA3_0(cc) ? 128 : 256);
                case GGML_TYPE_IQ2_XS:
                case GGML_TYPE_IQ2_S:
-                    return GGML_CUDA_CC_IS_RDNA3_5(cc) || ne11 <= 128;
+                    return GGML_CUDA_CC_IS_RDNA3_5(cc) || GGML_CUDA_CC_IS_RDNA4_M(cc) || ne11 <= 128;
                default:
                    return true;
            }
@@ -240,6 +240,9 @@ static __host__ ggml_cuda_mmq_config ggml_cuda_mmq_get_config(const ggml_type ty
        if (GGML_CUDA_CC_IS_RDNA4(cc)) {
            return ggml_cuda_mmq_get_config_rdna4(type, J, fallback);
        }
+        if (GGML_CUDA_CC_IS_RDNA4_M(cc)) {
+            return ggml_cuda_mmq_get_config_rdna3_5(type, J, fallback);
+        }
        if (GGML_CUDA_CC_IS_RDNA3_5(cc)) {
            return ggml_cuda_mmq_get_config_rdna3_5(type, J, fallback);
        }
@@ -272,6 +275,8 @@ static constexpr __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_config(ggml_t
    return ggml_cuda_mmq_get_config_cdna(type, J, fallback);
 #elif defined(RDNA4)
    return ggml_cuda_mmq_get_config_rdna4(type, J, fallback);
+#elif defined(RDNA4_M)
+    return ggml_cuda_mmq_get_config_rdna3_5(type, J, fallback);
 #elif defined(RDNA3_5)
    return ggml_cuda_mmq_get_config_rdna3_5(type, J, fallback);
 #elif defined(RDNA3)
@@ -844,7 +844,7 @@ bool ggml_cuda_should_use_mmvf(enum ggml_type type, int cc, int warp_size, const
                    return ne11 <= MMVF_MAX_BATCH_SIZE;
                }
                if (fp16_mma_hardware_available(cc)) {
-                    if (GGML_CUDA_CC_IS_RDNA3(cc)) {
+                    if (GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4_M(cc)) {
                        return ne11 <= 3;
                    }
                    if (GGML_CUDA_CC_IS_RDNA4(cc)) {
@@ -108,7 +108,7 @@ static constexpr __device__ mmvq_parameter_table_id get_device_table_id() {
    return MMVQ_PARAMETERS_RDNA4;
 #elif defined(RDNA3_0)
    return MMVQ_PARAMETERS_RDNA3_0;
-#elif defined(RDNA2) || defined(RDNA3_5)
+#elif defined(RDNA2) || defined(RDNA3_5) || defined(RDNA4_M)
    return MMVQ_PARAMETERS_RDNA2;
 #elif defined(GCN) || defined(CDNA)
    return MMVQ_PARAMETERS_GCN;
@@ -128,7 +128,7 @@ static __host__ mmvq_parameter_table_id get_device_table_id(int cc) {
    if (GGML_CUDA_CC_IS_RDNA3_0(cc)) {
        return MMVQ_PARAMETERS_RDNA3_0;
    }
-    if (GGML_CUDA_CC_IS_RDNA2(cc) || GGML_CUDA_CC_IS_RDNA3_5(cc)) {
+    if (GGML_CUDA_CC_IS_RDNA2(cc) || GGML_CUDA_CC_IS_RDNA3_5(cc) || GGML_CUDA_CC_IS_RDNA4_M(cc)) {
        return MMVQ_PARAMETERS_RDNA2;
    }
    if (GGML_CUDA_CC_IS_GCN(cc) || GGML_CUDA_CC_IS_CDNA(cc)) {
@@ -299,7 +299,7 @@ int get_mmvq_mmid_max_batch(ggml_type type, int cc) {
        if (GGML_CUDA_CC_IS_RDNA4(cc)) {
            return get_mmvq_mmid_max_batch_rdna4(type);
        }
-        if (GGML_CUDA_CC_IS_RDNA3(cc)) {
+        if (GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4_M(cc)) {
            return get_mmvq_mmid_max_batch_rdna3(type);
        }
        if (GGML_CUDA_CC_IS_RDNA1(cc) || GGML_CUDA_CC_IS_RDNA2(cc)) {
@@ -432,7 +432,7 @@ template <ggml_type type>
 static constexpr __device__ int get_mmvq_mmid_max_batch_for_device() {
 #if defined(RDNA4)
    return get_mmvq_mmid_max_batch_rdna4(type);
-#elif defined(RDNA3)
+#elif defined(RDNA3) || defined(RDNA4_M)
    return get_mmvq_mmid_max_batch_rdna3(type);
 #elif defined(RDNA2) || defined(RDNA1)
    return get_mmvq_mmid_max_batch_rdna1_rdna2(type);
@@ -83,21 +83,21 @@ static __global__ void quantize_q8_1(
    ggml_cuda_pdl_sync();
    const float xi = i0 < ne00 ? x[i03*s03 + i02*s02 + i01*s01 + i00] : 0.0f;
    float amax = fabsf(xi);
-    float sum = xi;

    amax = warp_reduce_max<QK8_1>(amax);
-    sum  = warp_reduce_sum<QK8_1>(sum);

    const float  d = amax / 127.0f;
    const int8_t q = amax == 0.0f ? 0 : roundf(xi / d);

+    const int sum = warp_reduce_sum<QK8_1>((int) q);
+
    y[ib].qs[iqs] = q;

    if (iqs > 0) {
        return;
    }

-    y[ib].ds = make_half2(d, sum);
+    y[ib].ds = make_half2(d, d * sum);
 }

 __device__ __forceinline__ uint8_t compute_e8m0_scale(float amax) {
@@ -500,17 +500,6 @@ static __global__ void quantize_mmq_q8_1(
        amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, offset, WARP_SIZE));
    }

-    float sum;
-    if (ds_layout != MMQ_Q8_1_DS_LAYOUT_D4) {
-        sum = xi.x + xi.y + xi.z + xi.w;
-
-        // Calculate sums across vals_per_sum/4 threads.
-#pragma unroll
-        for (int offset = vals_per_sum/8; offset > 0; offset >>= 1) {
-            sum += __shfl_xor_sync(0xFFFFFFFF, sum, offset, WARP_SIZE);
-        }
-    }
-
    const float d_inv = 127.0f / amax;
    char4 q;
    q.x = roundf(xi.x*d_inv);
@@ -519,6 +508,19 @@ static __global__ void quantize_mmq_q8_1(
    q.w = roundf(xi.w*d_inv);
    const float d = 1.0f / d_inv;

+    float sum = 0.0f;
+    if (ds_layout != MMQ_Q8_1_DS_LAYOUT_D4) {
+        int sum_q = int(q.x) + int(q.y) + int(q.z) + int(q.w);
+
+        // Calculate sums across vals_per_sum/4 threads.
+#pragma unroll
+        for (int offset = vals_per_sum/8; offset > 0; offset >>= 1) {
+            sum_q += __shfl_xor_sync(0xFFFFFFFF, sum_q, offset, WARP_SIZE);
+        }
+
+        sum = d * float(sum_q);
+    }
+
    // write the block once (normal) or to each of the token's compact rows (scatter)
    const int nwrite = scatter ? n_expert_used : 1;
 #pragma unroll
@@ -216,9 +216,13 @@
 #define RDNA4
 #endif // defined(__GFX12__)

-#if defined(__GFX11__)
+#if defined(__gfx1170__) || defined(__gfx1171__)
+#define RDNA4_M
+#endif // defined(__gfx1170__) || defined(__gfx1171__)
+
+#if defined(__GFX11__) && !defined(RDNA4_M)
 #define RDNA3
-#endif // defined(__GFX11__)
+#endif // defined(__GFX11__) && !defined(RDNA4_M)

 #if defined(__gfx1150__) || defined(__gfx1151__) || defined(__gfx1152__) || defined(__gfx1153__)
 #define RDNA3_5
@@ -237,9 +241,9 @@
 #define RDNA1
 #endif // defined(__gfx1010__) || defined(__gfx1012__)

-#if defined(RDNA4) || defined(RDNA3) || defined(RDNA2) || defined(RDNA1)
+#if defined(RDNA4) || defined(RDNA4_M) || defined(RDNA3) || defined(RDNA2) || defined(RDNA1)
 #define RDNA // For the entire family
-#endif // defined(RDNA4) || defined(RDNA3) || defined(RDNA2) || defined(RDNA1)
+#endif // defined(RDNA4) || defined(RDNA4_M) || defined(RDNA3) || defined(RDNA2) || defined(RDNA1)

 #ifndef __has_builtin
    #define __has_builtin(x) 0
@@ -254,6 +258,10 @@ typedef __hip_fp8_e4m3 __nv_fp8_e4m3;
 #define FP8_AVAILABLE
 #endif // HIP_VERSION >= 60300000

+#if HIP_VERSION < 70000000
+#define __ballot_sync(mask, var) __ballot(var)
+#endif //HIP_VERSION < 70000000
+
 typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
 typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4)));
 static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
@@ -373,6 +373,9 @@ static const struct ggml_backend_buffer_i ggml_backend_et_buffer_i = {
    /* .cpy_tensor      = */ ggml_backend_et_buffer_cpy_tensor,
    /* .clear           = */ ggml_backend_et_buffer_clear,
    /* .reset           = */ NULL,
+    /* .set_tensor_n    = */ NULL,
+    /* .get_tensor_n    = */ NULL,
+    /* .cpy_tensor_n    = */ NULL,
 };

 static const char * ggml_backend_et_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
@@ -1270,7 +1273,7 @@ static bool ggml_backend_et_device_supports_op(ggml_backend_dev_t dev, const ggm
        case GGML_OP_FLASH_ATTN_EXT:
            if (op->type == GGML_TYPE_F32 && op->src[0] && op->src[0]->type == GGML_TYPE_F32 && op->src[1] &&
                (op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F16) && op->src[2] &&
-                (op->src[2]->type == GGML_TYPE_F32 || op->src[2]->type == GGML_TYPE_F16) && op->src[4] == nullptr &&
+                (op->src[2]->type == GGML_TYPE_F32 || op->src[2]->type == GGML_TYPE_F16) && op->src[4] == nullptr && op->src[5] == nullptr &&
                ggml_is_contiguous_rows(op) && ggml_is_contiguous_rows(op->src[0])) {
                float max_bias      = 0.0f;
                float logit_softcap = 0.0f;
@@ -135,6 +135,7 @@ size_t opt_mbuf_total  = 0;                        // total buffer space limit (
 int    opt_mm_select     = 2; // 2 = HMX -> HVX -> CPU, 1 = HVX -> CPU, 0 = CPU (unsupported)
 int    opt_fa_select     = 2; // 2 = HMX -> HVX -> CPU, 1 = HVX -> CPU, 0 = CPU (unsupported)
 int    opt_fa_split      = 1; // 1 = head-split + team-split when n_kv_heads < N_cores (default), 0 = off
+int    opt_li_select     = 2; // 2 = HMX -> HVX, 1 = HVX, 0 = CPU (unsupported)
 int    opt_gdn_select    = 2; // 2 = HMX -> HVX, 1 = HVX, 0 = CPU (unsupported)
 int    opt_ar_select     = 2; // 2 = fused ALLREDUCE+ADD (default), 1 = unfused ALLREDUCE, 0 = fallback to CPY+FENCE
 int    opt_ar_scatter    = 1; // 1 = reduce-scatter the fused ALLREDUCE+ADD (default), 0 = full reduction
@@ -843,6 +844,9 @@ static ggml_backend_buffer_i ggml_backend_hexagon_buffer_interface = {
    /* .cpy_tensor      = */ ggml_backend_hexagon_buffer_cpy_tensor,
    /* .clear           = */ ggml_backend_hexagon_buffer_clear,
    /* .reset           = */ NULL,
+    /* .set_tensor_n    = */ NULL,
+    /* .get_tensor_n    = */ NULL,
+    /* .cpy_tensor_n    = */ NULL,
 };

 // ** backend buffer type
@@ -877,6 +881,9 @@ static ggml_backend_buffer_i ggml_backend_hexagon_host_buffer_interface = {
    /* .cpy_tensor      = */ ggml_backend_hexagon_buffer_cpy_tensor,
    /* .clear           = */ ggml_backend_hexagon_buffer_clear,
    /* .reset           = */ NULL,
+    /* .set_tensor_n    = */ NULL,
+    /* .get_tensor_n    = */ NULL,
+    /* .cpy_tensor_n    = */ NULL,
 };

 // ** backend buffer type
@@ -3403,6 +3410,7 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) {
    const char * str_mm_select = getenv("GGML_HEXAGON_MM_SELECT");
    const char * str_fa_select = getenv("GGML_HEXAGON_FA_SELECT");
    const char * str_fa_split  = getenv("GGML_HEXAGON_FA_SPLIT");
+    const char * str_li_select = getenv("GGML_HEXAGON_LI_SELECT");
    const char * str_gdn_select = getenv("GGML_HEXAGON_GDN_SELECT");
    const char * str_ar_select = getenv("GGML_HEXAGON_AR_SELECT");
    const char * str_ar_scatter = getenv("GGML_HEXAGON_AR_SCATTER");
@@ -3469,6 +3477,7 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) {
    opt_mm_select = str_mm_select ? atoi(str_mm_select)                   : opt_mm_select;
    opt_fa_select = str_fa_select ? atoi(str_fa_select)                   : opt_fa_select;
    opt_fa_split  = str_fa_split  ? atoi(str_fa_split)                   : opt_fa_split;
+    opt_li_select = str_li_select ? atoi(str_li_select)                   : opt_li_select;
    opt_gdn_select    = str_gdn_select    ? atoi(str_gdn_select)          : opt_gdn_select;
    opt_ar_select     = str_ar_select     ? atoi(str_ar_select)           : opt_ar_select;
    opt_ar_scatter    = str_ar_scatter    ? atoi(str_ar_scatter)          : opt_ar_scatter;
@@ -29,6 +29,7 @@ extern size_t opt_mbuf_total;
 extern int    opt_mm_select;
 extern int    opt_fa_select;
 extern int    opt_fa_split;
+extern int    opt_li_select;
 extern int    opt_gdn_select;
 extern int    opt_ar_select;
 extern int    opt_ar_scatter;
@@ -11,12 +11,14 @@
 #include "htp/cpy-ops.h"
 #include "htp/cumsum-ops.h"
 #include "htp/diag-ops.h"
+#include "htp/dsv4-hc-ops.h"
 #include "htp/fill-ops.h"
 #include "htp/flash-attn-ops.h"
 #include "htp/gated-delta-net-ops.h"
 #include "htp/get-rows-ops.h"
 #include "htp/glu-ops.h"
 #include "htp/im2col-ops.h"
+#include "htp/lightning-indexer-ops.h"
 #include "htp/matmul-ops.h"
 #include "htp/pad-ops.h"
 #include "htp/pool-ops.h"
@@ -28,7 +30,9 @@
 #include "htp/solve-tri-ops.h"
 #include "htp/ssm-conv.h"
 #include "htp/sum-rows-ops.h"
+#include "htp/top-k-mask-ops.h"
 #include "htp/unary-ops.h"
+#include "htp/upscale-ops.h"

 #include <type_traits>
 #include <utility>
@@ -134,4 +138,19 @@ bool htp_kparams_solve_unary_mul_glu(
    struct htp_glu_kernel_params * kparams
 );

+bool htp_kparams_solve_upscale(
+    const struct ggml_hexagon_session * sess,
+    const struct ggml_tensor * src0,
+    const struct ggml_tensor * dst,
+    struct htp_upscale_kernel_params * kparams
+);
+
+static inline bool htp_kparams_solve_upscale(
+    const struct ggml_hexagon_session * sess,
+    const struct ggml_tensor * op,
+    struct htp_upscale_kernel_params * kparams
+) {
+    return htp_kparams_solve_upscale(sess, op->src[0], op, kparams);
+}
+
 #endif // HTP_KPARAMS_H
@@ -5,11 +5,13 @@
 #include "htp/binary-ops.h"
 #include "htp/cumsum-ops.h"
 #include "htp/diag-ops.h"
+#include "htp/dsv4-hc-ops.h"
 #include "htp/fill-ops.h"
 #include "htp/flash-attn-ops.h"
 #include "htp/gated-delta-net-ops.h"
 #include "htp/get-rows-ops.h"
 #include "htp/glu-ops.h"
+#include "htp/lightning-indexer-ops.h"
 #include "htp/matmul-ops.h"
 #include "htp/pad-ops.h"
 #include "htp/repeat-ops.h"
@@ -18,7 +20,9 @@
 #include "htp/solve-tri-ops.h"
 #include "htp/ssm-conv.h"
 #include "htp/sum-rows-ops.h"
+#include "htp/top-k-mask-ops.h"
 #include "htp/unary-ops.h"
+#include "htp/upscale-ops.h"

 #include <stdio.h>

@@ -184,6 +188,22 @@ static std::string format_kernel_params(const htp_opnode & node) {
        const char * path = (kparams->kernel_type == HTP_GDN_KERNEL_HMX_CHUNKED) ? "hmx-chunked" : "hvx-recurrent";
        snprintf(str, max_size, "%s-%s vtcm %u", path, kparams->kda ? "kda" : "scalar",
                 (unsigned int) kparams->vtcm_size);
+    } else if (node.opcode == HTP_OP_DSV4_HC_PRE) {
+        const auto * kparams = node.kparams<htp_dsv4_hc_pre_kernel_params>();
+        const char * ktype_str = kparams->kernel_type == HTP_DSV4_HC_PRE_KERNEL_GATED ? "gated" : "plain";
+        snprintf(str, max_size, "%s nth %u vtcm %u", ktype_str,
+                 (unsigned int) kparams->n_threads, (unsigned int) kparams->vtcm_size);
+    } else if (node.opcode == HTP_OP_DSV4_HC_POST) {
+        const auto * kparams = node.kparams<htp_dsv4_hc_post_kernel_params>();
+        const char * ktype_str = kparams->kernel_type == HTP_DSV4_HC_POST_KERNEL_COMB ? "comb" : "identity";
+        snprintf(str, max_size, "%s nth %u vtcm %u", ktype_str,
+                 (unsigned int) kparams->n_threads, (unsigned int) kparams->vtcm_size);
+    } else if (node.opcode == HTP_OP_LIGHTNING_INDEXER) {
+        const auto * kparams = node.kparams<htp_lid_kernel_params>();
+        snprintf(str, max_size, "%s tok %u bc %u nth %u vtcm %u",
+                 kparams->kernel_type == HTP_LID_KERNEL_HMX ? "hmx" : "hvx",
+                 (unsigned int) kparams->n_tokens_tile, (unsigned int) kparams->kv_block,
+                 (unsigned int) kparams->n_threads, (unsigned int) kparams->vtcm_size);
    } else if (node.opcode == HTP_OP_MUL || node.opcode == HTP_OP_ADD || node.opcode == HTP_OP_ADD_ID ||
               node.opcode == HTP_OP_SUB || node.opcode == HTP_OP_DIV) {
        const auto * kparams = node.kparams<htp_binary_kernel_params>();
@@ -212,6 +232,14 @@ static std::string format_kernel_params(const htp_opnode & node) {
    } else if (node.opcode == HTP_OP_CUMSUM) {
        const auto * kparams = node.kparams<htp_cumsum_kernel_params>();
        snprintf(str, max_size, "%s nth %d vtcm %u", kparams->use_dma ? "dma" : "hvx", (int) kparams->n_threads, (unsigned int) kparams->vtcm_size);
+    } else if (node.opcode == HTP_OP_TOP_K_MASK) {
+        const auto * kparams = node.kparams<htp_top_k_mask_kernel_params>();
+        const char * ktype_str = "?";
+        switch (kparams->kernel_type) {
+            case HTP_TOP_K_MASK_KERNEL_ROWS:  ktype_str = "rows"; break;
+            case HTP_TOP_K_MASK_KERNEL_PICKS: ktype_str = "picks"; break;
+        }
+        snprintf(str, max_size, "%s nth %d tile %u x%u vtcm %u", ktype_str, (int) kparams->n_threads, (unsigned int) kparams->tile_cols, (unsigned int) kparams->n_col_tiles, (unsigned int) kparams->vtcm_size);
    } else if (node.opcode == HTP_OP_FILL) {
        const auto * kparams = node.kparams<htp_fill_kernel_params>();
        snprintf(str, max_size, "%s nth %d", kparams->kernel_type == HTP_FILL_KERNEL_CONTIGUOUS ? "contig" : "strided", (int) kparams->n_threads);
@@ -224,6 +252,19 @@ static std::string format_kernel_params(const htp_opnode & node) {
    } else if (node.opcode == HTP_OP_SOLVE_TRI) {
        const auto * kparams = node.kparams<htp_solve_tri_kernel_params>();
        snprintf(str, max_size, "%s nth %d", kparams->batched ? "batched" : "chunked", (int) kparams->n_threads);
+    } else if (node.opcode == HTP_OP_UPSCALE) {
+        const auto * kparams = node.kparams<htp_upscale_kernel_params>();
+        const char * ktype_str = "unknown";
+        switch (kparams->kernel_type) {
+            case HTP_UPSCALE_KERNEL_NEAREST:     ktype_str = "nearest";     break;
+            case HTP_UPSCALE_KERNEL_NEAREST_2X:  ktype_str = "nearest-2x";  break;
+            case HTP_UPSCALE_KERNEL_NEAREST_4X:  ktype_str = "nearest-4x";  break;
+            case HTP_UPSCALE_KERNEL_BILINEAR:    ktype_str = kparams->align_corners ? "bilinear-ac" : "bilinear"; break;
+            case HTP_UPSCALE_KERNEL_BILINEAR_2X: ktype_str = "bilinear-2x"; break;
+            case HTP_UPSCALE_KERNEL_BILINEAR_AA: ktype_str = "bilinear-aa"; break;
+            case HTP_UPSCALE_KERNEL_BICUBIC:     ktype_str = "bicubic";     break;
+        }
+        snprintf(str, max_size, "%s nth %d chk %d vtcm %u", ktype_str, (int) kparams->n_threads, (int) kparams->dst_nrows_per_chunk, (unsigned int) kparams->vtcm_size);
    } else {
        snprintf(str, max_size, "----");
    }
@@ -37,6 +37,7 @@ add_library(${HTP_LIB} SHARED
    repeat-ops.c
    ssm-conv.c
    cumsum-ops.c
+    top-k-mask-ops.c
    fill-ops.c
    concat-ops.c
    diag-ops.c
@@ -47,7 +48,10 @@ add_library(${HTP_LIB} SHARED
    conv2d-ops.c
    roll-ops.c
    pool-ops.c
+    upscale-ops.c
    allreduce-ops.c
+    dsv4-hc-ops.c
+    lightning-indexer-ops.c
 )

 target_compile_definitions(${HTP_LIB} PRIVATE
@@ -315,55 +315,61 @@ static void bitonic_sort_vtcm_desc(uint8_t * values, uint8_t * indices, uint32_t
    }
 }

-static void top_k_select_tiled(dma_queue * dma_q, struct htp_thread_trace * tr,
-                               dma_addr_t src_addr, uint32_t n, uint32_t k,
-                               float * values_buf, int32_t * indices_buf,
-                               float * out_values, int32_t * out_indices,
-                               uint32_t chunk_base) {
-    const uint32_t tile_elems = 1024;
-    uint32_t n_tiles            = (n + tile_elems - 1) / tile_elems;
-    uint32_t candidate_count    = n_tiles * k;
-    uint32_t merge_n_vec        = hmx_ceil_div(candidate_count, 32);
-    uint32_t merge_n_vec_pow2   = 1;
-    while (merge_n_vec_pow2 < merge_n_vec) merge_n_vec_pow2 <<= 1;
-    uint32_t merge_elems        = merge_n_vec_pow2 * 32;
-    float * candidate_values    = values_buf + tile_elems;
-    int32_t * candidate_indices = indices_buf + tile_elems;
-    uint32_t candidate_pos      = 0;
+// top-k of one row held in VTCM, k <= 64. Keeps a sorted list of the k best, rejects 256-float blocks with one max-reduce.
+// Writes k values and k indices (ties: first seen wins). Slots with no finite value get -INF and index 0.
+static void top_k_scan(const float * vals, uint32_t n, uint32_t k, float * out_v, int32_t * out_i) {
+    float   best_v[64];
+    int32_t best_i[64];
+    uint32_t cnt = 0;
+    float    thr = -INFINITY;

-    for (uint32_t offset = 0; offset < n; offset += tile_elems) {
-        uint32_t tile_count = MIN(tile_elems, n - offset);
-        dma_sync_read(dma_q, values_buf, src_addr + (size_t) offset * sizeof(float), tile_count * sizeof(float));
-
-        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) offset);
-        if (tile_count < tile_elems) {
-            hvx_splat_f32_u((uint8_t *) (values_buf + tile_count), -INFINITY, tile_elems - tile_count);
+    const uint32_t blk = 256;
+    for (uint32_t off = 0; off < n; off += blk) {
+        const uint32_t bn = MIN(blk, n - off);
+        if (hvx_reduce_max_f32((const uint8_t *) (vals + off), bn) <= thr) {
+            continue;
        }
-
-        bitonic_sort_vtcm_desc((uint8_t *) values_buf, (uint8_t *) indices_buf, tile_elems / 32, true);
-        uint32_t tile_k = MIN(k, tile_count);
-        for (uint32_t j = 0; j < tile_k; j++) {
-            candidate_values[candidate_pos] = values_buf[j];
-            candidate_indices[candidate_pos] = indices_buf[j] + (int32_t) offset;
-            candidate_pos++;
-        }
-        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) offset);
-    }
-
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, 0);
-    if (merge_elems > candidate_pos) {
-        hvx_splat_f32_u((uint8_t *) (candidate_values + candidate_pos), -INFINITY, merge_elems - candidate_pos);
-        for (uint32_t j = candidate_pos; j < merge_elems; j++) {
-            candidate_indices[j] = 0;
+        for (uint32_t vi = 0; vi < bn; vi += 32) {
+            const uint32_t vn = MIN(32, bn - vi);
+            HVX_VectorAlias v_local;
+            v_local.v = hvx_vmem(vals + off + vi);
+            for (uint32_t i = 0; i < vn; i++) {
+                const float v = v_local.fp32[i];
+                if (!(v > thr)) continue;
+                uint32_t pos = cnt < k ? cnt++ : k - 1;
+                while (pos > 0 && best_v[pos - 1] < v) {
+                    best_v[pos] = best_v[pos - 1];
+                    best_i[pos] = best_i[pos - 1];
+                    pos--;
+                }
+                best_v[pos] = v;
+                best_i[pos] = (int32_t) (off + vi + i);
+                if (cnt == k) thr = best_v[k - 1];
+            }
        }
    }

-    bitonic_sort_vtcm_desc((uint8_t *) candidate_values, (uint8_t *) candidate_indices, merge_n_vec_pow2, false);
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, 0);
+    // fewer than k values above -INF: take -INF or NaN elements, then pad with index 0
+    for (uint32_t off = 0; off < n && cnt < k; off += 32) {
+        const uint32_t vn = MIN(32, n - off);
+        HVX_VectorAlias v_local;
+        v_local.v = hvx_vmem(vals + off);
+        for (uint32_t j = 0; j < vn && cnt < k; j++) {
+            if (!(v_local.fp32[j] > -INFINITY)) {
+                best_v[cnt] = -INFINITY;
+                best_i[cnt] = (int32_t) (off + j);
+                cnt++;
+            }
+        }
+    }
+    for (; cnt < k; cnt++) {
+        best_v[cnt] = -INFINITY;
+        best_i[cnt] = 0;
+    }

    for (uint32_t j = 0; j < k; j++) {
-        out_values[j]  = candidate_values[j];
-        out_indices[j] = candidate_indices[j] + (int32_t) chunk_base;
+        out_v[j] = best_v[j];
+        out_i[j] = best_i[j];
    }
 }

@@ -509,10 +515,14 @@ static void htp_sort_chunk_job(unsigned int n, unsigned int i, void * data) {
        }

        if (local_k > 1 && local_k <= 64 && chunk_elems > 1024) {
-            top_k_select_tiled(octx->ctx->dma[i], tr, src_addr, real_count, local_k,
-                               values_buf, indices_buf,
-                               &merge_values[i * local_k], &merge_indices[i * local_k],
-                               chunk_base);
+            dma_sync_read(octx->ctx->dma[i], values_buf, src_addr, real_count * sizeof(float));
+
+            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) i);
+            top_k_scan(values_buf, real_count, local_k, &merge_values[i * local_k], &merge_indices[i * local_k]);
+            for (uint32_t j = 0; j < local_k; j++) {
+                merge_indices[i * local_k + j] += (int32_t) chunk_base;
+            }
+            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) i);
            return;
        }

@@ -668,10 +678,14 @@ static inline void compute_row_sort(
    uint32_t ne00,
    uint32_t chunk_elems,
    bool is_top_k,
-    enum ggml_sort_order order
+    enum ggml_sort_order order,
+    uint32_t k
 ) {
    if (is_top_k) {
-        if (ne00 == 32 || ne00 == 64 || ne00 == 128 || ne00 == 256 || ne00 == 512 || ne00 == 1024) {
+        if (k > 1 && k <= 64 && ne00 > 1024) {
+            float out_v[64];
+            top_k_scan(cur_values, ne00, k, out_v, cur_indices);
+        } else if (ne00 == 32 || ne00 == 64 || ne00 == 128 || ne00 == 256 || ne00 == 512 || ne00 == 1024) {
            switch (ne00) {
                case 32:   sort32_f32_hvx((uint8_t *) cur_values, (uint8_t *) cur_indices, GGML_SORT_ORDER_DESC); break;
                case 64:   sort64_f32_hvx((uint8_t *) cur_values, (uint8_t *) cur_indices, GGML_SORT_ORDER_DESC); break;
@@ -788,7 +802,7 @@ static void htp_sort_multi_row_job(unsigned int n, unsigned int ith, void * data
            dma_queue_pop(dma_q);

            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) r);
-            compute_row_sort(cur_values, cur_indices, ne00, chunk_elems, is_top_k, order);
+            compute_row_sort(cur_values, cur_indices, ne00, chunk_elems, is_top_k, order, k);
            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) r);

            // Push writeback of current slot
@@ -817,7 +831,7 @@ static void htp_sort_multi_row_job(unsigned int n, unsigned int ith, void * data
            dma_sync_read(dma_q, values_buf, src_addr, src_bytes);

            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) r);
-            compute_row_sort(values_buf, indices_buf, ne00, chunk_elems, is_top_k, order);
+            compute_row_sort(values_buf, indices_buf, ne00, chunk_elems, is_top_k, order, k);
            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) r);

            dma_sync_write(dma_q, dst_addr, indices_buf, dst_bytes);
@@ -47,87 +47,108 @@ static void concat_2d_f32_transposed(unsigned int nth, unsigned int ith, void *
    const uint32_t src0_ne0 = src0->ne[0];
    const uint32_t src1_ne0 = src1->ne[0];

-    const uint32_t row_end = cctx->row_start + cctx->nrows;
-    const uint32_t start_i = cctx->row_start + ith * cctx->nrows_per_thread;
-    const uint32_t end_i   = (start_i + cctx->nrows_per_thread < row_end) ? (start_i + cctx->nrows_per_thread) : row_end;
-    if (start_i >= end_i) return;
+    // dst rows [row_begin, row_end) of this thread; a dst row is a src0 row followed by a src1 column
+    const uint32_t row_begin = cctx->row_start + ith * cctx->nrows_per_thread;
+    const uint32_t row_end   = MIN(row_begin + cctx->nrows_per_thread, cctx->row_start + cctx->nrows);
+    if (row_begin >= row_end) return;

    dma_queue * dma_q = octx->ctx->dma[ith];
-
-    uint8_t * spad0_base = cctx->spad0_base + ith * cctx->spad0_size_per_thread;
-    uint8_t * spad1_base = cctx->spad1_base + ith * cctx->spad1_size_per_thread;
-
-    const uint32_t block_i = 32;
-    const uint32_t spad1_stride = block_i * sizeof(float);
-
-    const HVX_Vector offsets = hvx_vec_gather_offsets_w(spad1_stride);
-    const uint32_t src1_ne0_padded = hex_round_up(src1_ne0, 32);
-    const uint32_t src0_row_bytes  = src0_ne0 * sizeof(float);
-    const uint32_t src0_row_padded = hex_round_up(src0_row_bytes, VLEN);
-    const uint32_t src0_pre        = src0_row_padded - src0_row_bytes;
-    const uint32_t spad0_row_bytes = src0_row_padded + src1_ne0_padded * sizeof(float);
-
    struct htp_thread_trace * tr = &octx->ctx->trace[ith];

    const struct fastdiv_values * div_ne2 = &cctx->div_ne2;
    const uint32_t ne2 = dst->ne[2];

-    uint32_t p = 0;
-    uint32_t i = start_i;
+    // tile: block_rows dst rows x block_cols src1 columns
+    const uint32_t block_rows = 128;
+    const uint32_t block_cols = 256;
+    // +1 element spreads the gather lanes across VTCM, so they do not serialize on one path
+    const uint32_t spad1_stride = block_rows * sizeof(float) + sizeof(float);

-    const dma_addr_t src1_addr = concat_plane_addr(src1, p, div_ne2, ne2) + i * src1->nb[1];
-    dma_queue_push(dma_q, dma_make_data(spad1_base, src1_addr), spad1_stride, src1->nb[0], MIN(end_i - i, block_i) * sizeof(float), src1_ne0);
+    const HVX_Vector offsets = hvx_vec_gather_offsets_w(spad1_stride);
+    const uint32_t block_cols_padded = MIN(hex_round_up(src1_ne0, 32), block_cols);
+    const uint32_t src0_row_bytes    = src0_ne0 * sizeof(float);
+    const uint32_t src0_row_padded   = hex_round_up(src0_row_bytes, VLEN);
+    const uint32_t src0_pre          = src0_row_padded - src0_row_bytes;
+    const uint32_t spad0_stride      = src0_row_padded + block_cols_padded * sizeof(float);

-    const dma_addr_t src0_addr = concat_plane_addr(src0, p, div_ne2, ne2) + i * src0->nb[1];
-    dma_queue_push(dma_q, dma_make_data(spad0_base + src0_pre, src0_addr), spad0_row_bytes, src0->nb[1], src0_row_bytes, MIN(end_i - i, block_i));
+    // two slots per scratchpad: tile b gathers in slot b&1 while dst[b-1] and src1[b+1] DMA in the other
+    uint8_t * spad0_base = cctx->spad0_base + ith * cctx->spad0_size_per_thread;
+    uint8_t * spad1_base = cctx->spad1_base + ith * cctx->spad1_size_per_thread;
+    const uint32_t spad0_slot = block_rows * spad0_stride;
+    const uint32_t spad1_slot = block_cols_padded * spad1_stride;

-    dma_queue_pop(dma_q); // src1
+    // tiles walk (plane, row, col) with col fastest; src0 goes out with the col == 0 tile only
+    uint32_t plane = 0;
+    uint32_t row   = row_begin;
+    uint32_t col   = 0;
+    uint32_t slot  = 0;
+    bool dst_pending = false;

-    while (p < cctx->nplanes) {
-        const uint32_t current_block_i = MIN(end_i - i, block_i);
+    const dma_addr_t src1_addr = concat_plane_addr(src1, plane, div_ne2, ne2) + row * src1->nb[1];
+    dma_queue_push(dma_q, dma_make_data(spad1_base, src1_addr), spad1_stride, src1->nb[0], MIN(row_end - row, block_rows) * sizeof(float), MIN(src1_ne0, block_cols));

-        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) i);
-        for (uint32_t j = 0; j < src1_ne0; j += 32) {
-            const uint8_t * src_ptr = spad1_base + j * spad1_stride;
-            uint8_t * dst_ptr = spad0_base + src0_row_padded + j * sizeof(float);
-            hvx_transpose_32x32_w_gather(dst_ptr, spad0_row_bytes, src_ptr, spad1_stride, offsets, current_block_i, MIN(src1_ne0 - j, 32));
+    const dma_addr_t src0_addr = concat_plane_addr(src0, plane, div_ne2, ne2) + row * src0->nb[1];
+    dma_queue_push(dma_q, dma_make_data(spad0_base + src0_pre, src0_addr), spad0_stride, src0->nb[1], src0_row_bytes, MIN(row_end - row, block_rows));
+
+    while (plane < cctx->nplanes) {
+        const uint32_t nrows = MIN(row_end - row, block_rows);
+        const uint32_t ncols = MIN(src1_ne0 - col, block_cols);
+
+        uint8_t * spad0 = spad0_base + slot * spad0_slot;
+        uint8_t * spad1 = spad1_base + slot * spad1_slot;
+
+        dma_queue_pop(dma_q); // src1
+        if (col == 0) {
+            dma_queue_pop(dma_q); // src0
        }
-        hvx_gather_sync(spad0_base + src0_row_padded);
-        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) i);

-        uint32_t np = p;
-        uint32_t ni = i + block_i;
-        if (ni >= end_i) {
-            ni = start_i;
-            np++;
+        uint32_t next_plane = plane;
+        uint32_t next_row   = row;
+        uint32_t next_col   = col + block_cols;
+        if (next_col >= src1_ne0) {
+            next_col = 0;
+            next_row = row + block_rows;
+            if (next_row >= row_end) {
+                next_row = row_begin;
+                next_plane++;
+            }
        }
-        const bool has_next = np < cctx->nplanes;
-        const uint32_t next_block_i = MIN(end_i - ni, block_i);
+        const bool has_next = next_plane < cctx->nplanes;
+        const uint32_t next_nrows = MIN(row_end - next_row, block_rows);
+        const uint32_t next_ncols = MIN(src1_ne0 - next_col, block_cols);

-        // spad1 is free after the gather sync, prefetch next src1 ahead of the dst write
+        // other spad1 slot is free since the previous gather sync
        if (has_next) {
-            const dma_addr_t nsrc1_addr = concat_plane_addr(src1, np, div_ne2, ne2) + ni * src1->nb[1];
-            dma_queue_push(dma_q, dma_make_data(spad1_base, nsrc1_addr), spad1_stride, src1->nb[0], next_block_i * sizeof(float), src1_ne0);
+            const dma_addr_t addr = concat_plane_addr(src1, next_plane, div_ne2, ne2) + next_row * src1->nb[1] + next_col * src1->nb[0];
+            dma_queue_push(dma_q, dma_make_data(spad1_base + (slot ^ 1) * spad1_slot, addr), spad1_stride, src1->nb[0], next_nrows * sizeof(float), next_ncols);
        }

-        dma_queue_pop(dma_q); // src0
-
-        const dma_addr_t dst_addr = concat_plane_addr(dst, p, div_ne2, ne2) + i * dst->nb[1];
-        dma_queue_push(dma_q, dma_make_data(dst_addr, spad0_base + src0_pre), dst->nb[1], spad0_row_bytes, (src0_ne0 + src1_ne0) * sizeof(float), current_block_i);
-
-        if (has_next) {
-            dma_queue_pop(dma_q); // next src1
+        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) row);
+        for (uint32_t c = 0; c < ncols; c += 32) {
+            hvx_transpose_32x32_w_gather(spad0 + src0_row_padded + c * sizeof(float), spad0_stride, spad1 + c * spad1_stride, spad1_stride, offsets, nrows, MIN(ncols - c, 32));
        }
-        dma_queue_pop(dma_q); // dst
+        hvx_gather_sync(spad0 + src0_row_padded);
+        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) row);

-        // spad0 is free after the dst write
-        if (has_next) {
-            const dma_addr_t nsrc0_addr = concat_plane_addr(src0, np, div_ne2, ne2) + ni * src0->nb[1];
-            dma_queue_push(dma_q, dma_make_data(spad0_base + src0_pre, nsrc0_addr), spad0_row_bytes, src0->nb[1], src0_row_bytes, next_block_i);
+        // other spad0 slot is free once dst[b-1] is written
+        if (dst_pending) {
+            dma_queue_pop(dma_q); // dst
+        }
+        if (has_next && next_col == 0) {
+            const dma_addr_t addr = concat_plane_addr(src0, next_plane, div_ne2, ne2) + next_row * src0->nb[1];
+            dma_queue_push(dma_q, dma_make_data(spad0_base + (slot ^ 1) * spad0_slot + src0_pre, addr), spad0_stride, src0->nb[1], src0_row_bytes, next_nrows);
        }

-        p = np;
-        i = ni;
+        // the col == 0 tile also carries the src0 columns in front
+        const uint32_t lead = (col == 0) ? src0_row_bytes : 0;
+        const dma_addr_t dst_addr = concat_plane_addr(dst, plane, div_ne2, ne2) + row * dst->nb[1] + src0_row_bytes + col * sizeof(float) - lead;
+        dma_queue_push(dma_q, dma_make_data(dst_addr, spad0 + src0_row_padded - lead), dst->nb[1], spad0_stride, lead + ncols * sizeof(float), nrows);
+        dst_pending = true;
+
+        plane = next_plane;
+        row   = next_row;
+        col   = next_col;
+        slot ^= 1;
    }
    dma_queue_flush(dma_q);
 }
@@ -143,87 +164,108 @@ static void concat_2d_f16_transposed(unsigned int nth, unsigned int ith, void *
    const uint32_t src0_ne0 = src0->ne[0];
    const uint32_t src1_ne0 = src1->ne[0];

-    const uint32_t row_end = cctx->row_start + cctx->nrows;
-    const uint32_t start_i = cctx->row_start + ith * cctx->nrows_per_thread;
-    const uint32_t end_i   = (start_i + cctx->nrows_per_thread < row_end) ? (start_i + cctx->nrows_per_thread) : row_end;
-    if (start_i >= end_i) return;
+    // dst rows [row_begin, row_end) of this thread; a dst row is a src0 row followed by a src1 column
+    const uint32_t row_begin = cctx->row_start + ith * cctx->nrows_per_thread;
+    const uint32_t row_end   = MIN(row_begin + cctx->nrows_per_thread, cctx->row_start + cctx->nrows);
+    if (row_begin >= row_end) return;

    dma_queue * dma_q = octx->ctx->dma[ith];
-
-    uint8_t * spad0_base = cctx->spad0_base + ith * cctx->spad0_size_per_thread;
-    uint8_t * spad1_base = cctx->spad1_base + ith * cctx->spad1_size_per_thread;
-
-    const uint32_t block_i = 64;
-    const uint32_t spad1_stride = block_i * sizeof(__fp16);
-
-    const HVX_Vector offsets = hvx_vec_gather_offsets_h(spad1_stride);
-    const uint32_t src1_ne0_padded = hex_round_up(src1_ne0, 64);
-    const uint32_t src0_row_bytes  = src0_ne0 * sizeof(__fp16);
-    const uint32_t src0_row_padded = hex_round_up(src0_row_bytes, VLEN);
-    const uint32_t src0_pre        = src0_row_padded - src0_row_bytes;
-    const uint32_t spad0_row_bytes = src0_row_padded + src1_ne0_padded * sizeof(__fp16);
-
    struct htp_thread_trace * tr = &octx->ctx->trace[ith];

    const struct fastdiv_values * div_ne2 = &cctx->div_ne2;
    const uint32_t ne2 = dst->ne[2];

-    uint32_t p = 0;
-    uint32_t i = start_i;
+    // tile: block_rows dst rows x block_cols src1 columns
+    const uint32_t block_rows = 128;
+    const uint32_t block_cols = 512;
+    // +1 element spreads the gather lanes across VTCM, so they do not serialize on one path
+    const uint32_t spad1_stride = block_rows * sizeof(__fp16) + sizeof(__fp16);

-    const dma_addr_t src1_addr = concat_plane_addr(src1, p, div_ne2, ne2) + i * src1->nb[1];
-    dma_queue_push(dma_q, dma_make_data(spad1_base, src1_addr), spad1_stride, src1->nb[0], MIN(end_i - i, block_i) * sizeof(__fp16), src1_ne0);
+    const HVX_Vector offsets = hvx_vec_gather_offsets_h(spad1_stride);
+    const uint32_t block_cols_padded = MIN(hex_round_up(src1_ne0, 64), block_cols);
+    const uint32_t src0_row_bytes    = src0_ne0 * sizeof(__fp16);
+    const uint32_t src0_row_padded   = hex_round_up(src0_row_bytes, VLEN);
+    const uint32_t src0_pre          = src0_row_padded - src0_row_bytes;
+    const uint32_t spad0_stride      = src0_row_padded + block_cols_padded * sizeof(__fp16);

-    const dma_addr_t src0_addr = concat_plane_addr(src0, p, div_ne2, ne2) + i * src0->nb[1];
-    dma_queue_push(dma_q, dma_make_data(spad0_base + src0_pre, src0_addr), spad0_row_bytes, src0->nb[1], src0_row_bytes, MIN(end_i - i, block_i));
+    // two slots per scratchpad: tile b gathers in slot b&1 while dst[b-1] and src1[b+1] DMA in the other
+    uint8_t * spad0_base = cctx->spad0_base + ith * cctx->spad0_size_per_thread;
+    uint8_t * spad1_base = cctx->spad1_base + ith * cctx->spad1_size_per_thread;
+    const uint32_t spad0_slot = block_rows * spad0_stride;
+    const uint32_t spad1_slot = block_cols_padded * spad1_stride;

-    dma_queue_pop(dma_q); // src1
+    // tiles walk (plane, row, col) with col fastest; src0 goes out with the col == 0 tile only
+    uint32_t plane = 0;
+    uint32_t row   = row_begin;
+    uint32_t col   = 0;
+    uint32_t slot  = 0;
+    bool dst_pending = false;

-    while (p < cctx->nplanes) {
-        const uint32_t current_block_i = MIN(end_i - i, block_i);
+    const dma_addr_t src1_addr = concat_plane_addr(src1, plane, div_ne2, ne2) + row * src1->nb[1];
+    dma_queue_push(dma_q, dma_make_data(spad1_base, src1_addr), spad1_stride, src1->nb[0], MIN(row_end - row, block_rows) * sizeof(__fp16), MIN(src1_ne0, block_cols));

-        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) i);
-        for (uint32_t j = 0; j < src1_ne0; j += 64) {
-            const uint8_t * src_ptr = spad1_base + j * spad1_stride;
-            uint8_t * dst_ptr = spad0_base + src0_row_padded + j * sizeof(__fp16);
-            hvx_transpose_64x64_h_gather(dst_ptr, spad0_row_bytes, src_ptr, spad1_stride, offsets, current_block_i, MIN(src1_ne0 - j, 64));
+    const dma_addr_t src0_addr = concat_plane_addr(src0, plane, div_ne2, ne2) + row * src0->nb[1];
+    dma_queue_push(dma_q, dma_make_data(spad0_base + src0_pre, src0_addr), spad0_stride, src0->nb[1], src0_row_bytes, MIN(row_end - row, block_rows));
+
+    while (plane < cctx->nplanes) {
+        const uint32_t nrows = MIN(row_end - row, block_rows);
+        const uint32_t ncols = MIN(src1_ne0 - col, block_cols);
+
+        uint8_t * spad0 = spad0_base + slot * spad0_slot;
+        uint8_t * spad1 = spad1_base + slot * spad1_slot;
+
+        dma_queue_pop(dma_q); // src1
+        if (col == 0) {
+            dma_queue_pop(dma_q); // src0
        }
-        hvx_gather_sync(spad0_base + src0_row_padded);
-        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) i);

-        uint32_t np = p;
-        uint32_t ni = i + block_i;
-        if (ni >= end_i) {
-            ni = start_i;
-            np++;
+        uint32_t next_plane = plane;
+        uint32_t next_row   = row;
+        uint32_t next_col   = col + block_cols;
+        if (next_col >= src1_ne0) {
+            next_col = 0;
+            next_row = row + block_rows;
+            if (next_row >= row_end) {
+                next_row = row_begin;
+                next_plane++;
+            }
        }
-        const bool has_next = np < cctx->nplanes;
-        const uint32_t next_block_i = MIN(end_i - ni, block_i);
+        const bool has_next = next_plane < cctx->nplanes;
+        const uint32_t next_nrows = MIN(row_end - next_row, block_rows);
+        const uint32_t next_ncols = MIN(src1_ne0 - next_col, block_cols);

-        // spad1 is free after the gather sync, prefetch next src1 ahead of the dst write
+        // other spad1 slot is free since the previous gather sync
        if (has_next) {
-            const dma_addr_t nsrc1_addr = concat_plane_addr(src1, np, div_ne2, ne2) + ni * src1->nb[1];
-            dma_queue_push(dma_q, dma_make_data(spad1_base, nsrc1_addr), spad1_stride, src1->nb[0], next_block_i * sizeof(__fp16), src1_ne0);
+            const dma_addr_t addr = concat_plane_addr(src1, next_plane, div_ne2, ne2) + next_row * src1->nb[1] + next_col * src1->nb[0];
+            dma_queue_push(dma_q, dma_make_data(spad1_base + (slot ^ 1) * spad1_slot, addr), spad1_stride, src1->nb[0], next_nrows * sizeof(__fp16), next_ncols);
        }

-        dma_queue_pop(dma_q); // src0
-
-        const dma_addr_t dst_addr = concat_plane_addr(dst, p, div_ne2, ne2) + i * dst->nb[1];
-        dma_queue_push(dma_q, dma_make_data(dst_addr, spad0_base + src0_pre), dst->nb[1], spad0_row_bytes, (src0_ne0 + src1_ne0) * sizeof(__fp16), current_block_i);
-
-        if (has_next) {
-            dma_queue_pop(dma_q); // next src1
+        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) row);
+        for (uint32_t c = 0; c < ncols; c += 64) {
+            hvx_transpose_64x64_h_gather(spad0 + src0_row_padded + c * sizeof(__fp16), spad0_stride, spad1 + c * spad1_stride, spad1_stride, offsets, nrows, MIN(ncols - c, 64));
        }
-        dma_queue_pop(dma_q); // dst
+        hvx_gather_sync(spad0 + src0_row_padded);
+        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) row);

-        // spad0 is free after the dst write
-        if (has_next) {
-            const dma_addr_t nsrc0_addr = concat_plane_addr(src0, np, div_ne2, ne2) + ni * src0->nb[1];
-            dma_queue_push(dma_q, dma_make_data(spad0_base + src0_pre, nsrc0_addr), spad0_row_bytes, src0->nb[1], src0_row_bytes, next_block_i);
+        // other spad0 slot is free once dst[b-1] is written
+        if (dst_pending) {
+            dma_queue_pop(dma_q); // dst
+        }
+        if (has_next && next_col == 0) {
+            const dma_addr_t addr = concat_plane_addr(src0, next_plane, div_ne2, ne2) + next_row * src0->nb[1];
+            dma_queue_push(dma_q, dma_make_data(spad0_base + (slot ^ 1) * spad0_slot + src0_pre, addr), spad0_stride, src0->nb[1], src0_row_bytes, next_nrows);
        }

-        p = np;
-        i = ni;
+        // the col == 0 tile also carries the src0 columns in front
+        const uint32_t lead = (col == 0) ? src0_row_bytes : 0;
+        const dma_addr_t dst_addr = concat_plane_addr(dst, plane, div_ne2, ne2) + row * dst->nb[1] + src0_row_bytes + col * sizeof(__fp16) - lead;
+        dma_queue_push(dma_q, dma_make_data(dst_addr, spad0 + src0_row_padded - lead), dst->nb[1], spad0_stride, lead + ncols * sizeof(__fp16), nrows);
+        dst_pending = true;
+
+        plane = next_plane;
+        row   = next_row;
+        col   = next_col;
+        slot ^= 1;
    }
    dma_queue_flush(dma_q);
 }
@@ -53,13 +53,17 @@ static inline void htp_concat_transposed_vtcm_layout_build(
    uint32_t type_size,
    uint32_t n_threads) {

-    uint32_t block_i = (type_size == 4) ? 32 : 64;
-    uint32_t spad1_stride = block_i * type_size;
-    uint32_t src1_ne0_padded = hex_round_up(src1_ne0, block_i);
-    uint32_t spad0_row_bytes = hex_round_up(src0_ne0 * type_size, 128) + src1_ne0_padded * type_size;
+    // src1_ne0 is processed in chunks of block_cols tokens, so VTCM does not grow with the batch
+    uint32_t block_rows = 128;
+    uint32_t block_cols = (type_size == 4) ? 256 : 512;
+    // +1 element spreads the gather lanes across VTCM, so they do not serialize on one path
+    uint32_t spad1_stride = block_rows * type_size + type_size;
+    uint32_t block_cols_padded = MIN(hex_round_up(src1_ne0, (type_size == 4) ? 32 : 64), block_cols);
+    uint32_t spad0_row_bytes = hex_round_up(src0_ne0 * type_size, 128) + block_cols_padded * type_size;

-    L->src0_spad_size_per_thread = block_i * spad0_row_bytes;
-    L->src1_spad_size_per_thread = src1_ne0_padded * spad1_stride;
+    // double-buffered: two slots per thread
+    L->src0_spad_size_per_thread = 2 * block_rows * spad0_row_bytes;
+    L->src1_spad_size_per_thread = 2 * block_cols_padded * spad1_stride;

    size_t off = 0;
    VTCM_LAYOUT_ALLOC(off, off_spad0, (size_t) n_threads * L->src0_spad_size_per_thread);
@@ -35,6 +35,10 @@ struct htp_copy_context {
    struct htp_copy_convert_vtcm_layout layout;
    uint8_t * vtcm_src0;
    uint8_t * vtcm_dst;
+
+    uint32_t tile_start;
+    uint32_t ntiles;
+    uint32_t tiles_per_thread;
 };

 #define cpy_preamble                              \
@@ -162,6 +166,171 @@ static inline void cpy_cursor_advance(struct cpy_row_cursor * c, uint32_t n, uin
    }
 }

+struct cpy_tile_cursor {
+    uint32_t ibk;
+    uint32_t ib0;
+    uint32_t i2;
+    uint32_t i3;
+};
+
+static inline void cpy_tile_cursor_advance(struct cpy_tile_cursor * c, const struct htp_copy_transpose_params * tp) {
+    if (++c->ibk == tp->nkb) {
+        c->ibk = 0;
+        if (++c->ib0 == tp->n0b) {
+            c->ib0 = 0;
+            if (++c->i2 == tp->ne[2]) {
+                c->i2 = 0;
+                c->i3++;
+            }
+        }
+    }
+}
+
+// Gather offsets for element e = l >> log2_m at (e % r, e / r)
+static inline HVX_Vector cpy_transpose_offsets(const struct htp_copy_transpose_params * tp) {
+    const uint32_t r_mask = (1u << tp->log2_r) - 1;
+    if (tp->elem_size != 2) {
+        const uint32_t m_mask = (1u << tp->log2_m) - 1;
+        int32_t offsets[32] __attribute__((aligned(128)));
+        for (uint32_t l = 0; l < 32; l++) {
+            const uint32_t e = l >> tp->log2_m;
+            offsets[l] = (e & r_mask) * tp->in_stride + (e >> tp->log2_r) * tp->elem_size + (l & m_mask) * 4;
+        }
+        return *(HVX_Vector *) offsets;
+    }
+    int16_t offsets[64] __attribute__((aligned(128)));
+    for (uint32_t l = 0; l < 64; l++) {
+        offsets[l] = (l & r_mask) * tp->in_stride + (l >> tp->log2_r) * 2;
+    }
+    return *(HVX_Vector *) offsets;
+}
+
+static inline void cpy_transpose_push_in(dma_queue * q, const struct htp_copy_transpose_params * tp, dma_addr_t src,
+                                         const struct cpy_tile_cursor * c, uint8_t * buf) {
+    const uint32_t a0  = c->ib0 * tp->blk0;
+    const uint32_t ak  = c->ibk * tp->blkk;
+    const uint32_t off = a0 * tp->src_nb[0] + ak * tp->elem_size + c->i2 * tp->src_nb[1] + c->i3 * tp->src_nb[2];
+    dma_queue_push(q, dma_make_data(buf, src + off), tp->in_stride, tp->src_nb[0],
+                   MIN(tp->blkk, tp->ne[1] - ak) * tp->elem_size, MIN(tp->blk0, tp->ne[0] - a0));
+}
+
+// Pipelined tile worker: DMA in -> vgather transpose -> DMA out
+static void cpy_thread_transpose(unsigned int nth, unsigned int ith, void * data) {
+    struct htp_copy_context * ct = (struct htp_copy_context *) data;
+    struct htp_ops_context * octx = ct->octx;
+    const struct htp_copy_transpose_params * tp = &ct->kparams->u.transpose;
+
+    const uint32_t t0 = ct->tile_start + ith * ct->tiles_per_thread;
+    const uint32_t t1 = MIN(t0 + ct->tiles_per_thread, ct->tile_start + ct->ntiles);
+    if (t0 >= t1) {
+        return;
+    }
+
+    dma_queue * dma_q = octx->ctx->dma[ith];
+    struct htp_thread_trace * tr = &octx->ctx->trace[ith];
+
+    const dma_addr_t src = octx->src[0]->data;
+    const dma_addr_t dst = octx->dst->data;
+
+    const uint32_t es         = tp->elem_size;
+    const uint32_t ne0        = tp->ne[0];
+    const uint32_t nek        = tp->ne[1];
+    const uint32_t in_stride  = tp->in_stride;
+    const uint32_t out_stride = tp->out_stride;
+
+    const HVX_Vector offsets   = cpy_transpose_offsets(tp);
+    const uint32_t packed_mu   = (ne0 - 1) * in_stride + 127;
+    const uint32_t packed_step = 128 >> tp->log2_r;
+    const uint32_t epv         = 1u << tp->log2_r;
+
+    uint8_t * in_buf[2];
+    uint8_t * out_buf[2];
+    in_buf[0]  = ct->vtcm_src0 + ith * 2 * tp->in_buf_size;
+    in_buf[1]  = in_buf[0] + tp->in_buf_size;
+    out_buf[0] = ct->vtcm_dst + ith * 2 * tp->out_buf_size;
+    out_buf[1] = out_buf[0] + tp->out_buf_size;
+
+    struct cpy_tile_cursor cur;
+    {
+        const uint32_t q0 = fastdiv(t0, &tp->div_nkb);
+        const uint32_t q1 = fastdiv(q0, &tp->div_n0b);
+        cur.ibk = t0 - q0 * tp->nkb;
+        cur.ib0 = q0 - q1 * tp->n0b;
+        cur.i3  = fastdiv(q1, &tp->div_ne2);
+        cur.i2  = q1 - cur.i3 * tp->ne[2];
+    }
+
+    struct cpy_tile_cursor nxt = cur;
+
+    cpy_transpose_push_in(dma_q, tp, src, &cur, in_buf[0]);
+
+    for (uint32_t t = t0, i = 0; t < t1; t++, i++) {
+        while (dma_queue_depth(dma_q) > (i > 0 ? 1u : 0u)) {
+            dma_queue_pop(dma_q);
+        }
+
+        uint8_t * in  = in_buf[i & 1];
+        uint8_t * out = out_buf[i & 1];
+
+        const uint32_t a0 = cur.ib0 * tp->blk0;
+        const uint32_t ak = cur.ibk * tp->blkk;
+        const uint32_t n0 = MIN(tp->blk0, ne0 - a0);
+        const uint32_t nk = MIN(tp->blkk, nek - ak);
+
+        if (t + 1 < t1) {
+            cpy_tile_cursor_advance(&nxt, tp);
+            cpy_transpose_push_in(dma_q, tp, src, &nxt, in_buf[(i + 1) & 1]);
+        }
+
+        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) t);
+        if (tp->packed) {
+            const uint32_t nvec = (nk * ne0 * es + 127) >> 7;
+            if (es != 2) {
+                #pragma unroll(4)
+                for (uint32_t v = 0; v < nvec; v++) {
+                    Q6_vgather_ARMVw((HVX_Vector *) (out + v * 128), (size_t) (in + v * packed_step), packed_mu, offsets);
+                }
+            } else {
+                #pragma unroll(4)
+                for (uint32_t v = 0; v < nvec; v++) {
+                    Q6_vgather_ARMVh((HVX_Vector *) (out + v * 128), (size_t) (in + v * packed_step), packed_mu, offsets);
+                }
+            }
+        } else {
+            for (uint32_t rb = 0; rb < n0; rb += epv) {
+                const uint32_t  mu = (MIN(epv, n0 - rb) - 1) * in_stride + es - 1;
+                uint8_t *       d  = out + rb * es;
+                const uint8_t * s  = in + rb * in_stride;
+                if (es != 2) {
+                    #pragma unroll(4)
+                    for (uint32_t ik = 0; ik < nk; ik++) {
+                        Q6_vgather_ARMVw((HVX_Vector *) (d + ik * out_stride), (size_t) (s + ik * es), mu, offsets);
+                    }
+                } else {
+                    #pragma unroll(4)
+                    for (uint32_t ik = 0; ik < nk; ik++) {
+                        Q6_vgather_ARMVh((HVX_Vector *) (d + ik * out_stride), (size_t) (s + ik * 2), mu, offsets);
+                    }
+                }
+            }
+        }
+        hvx_gather_sync(out);
+        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) t);
+
+        const uint32_t doff = a0 * es + ak * tp->dst_nb[0] + cur.i2 * tp->dst_nb[1] + cur.i3 * tp->dst_nb[2];
+        if (tp->packed) {
+            const uint32_t n = nk * ne0 * es;
+            dma_queue_push(dma_q, dma_make_data(dst + doff, out), n, n, n, 1);
+        } else {
+            dma_queue_push(dma_q, dma_make_data(dst + doff, out), tp->dst_nb[0], out_stride, n0 * es, nk);
+        }
+
+        cur = nxt;
+    }
+
+    dma_queue_flush(dma_q);
+}
+
 #define DEFINE_CPY_CONVERT_SAMESHAPE(NAME, CONV_FUNC)                                        \
 static void cpy_thread_##NAME##_sameshape(unsigned int nth, unsigned int ith, void * data) { \
    struct htp_copy_context * ct = (struct htp_copy_context *) data;                         \
@@ -439,6 +608,48 @@ static int cpy_reshape(struct htp_ops_context * octx, const struct htp_copy_kern
    return HTP_STATUS_OK;
 }

+static int cpy_transpose(struct htp_ops_context * octx, const struct htp_copy_kernel_params * kparams) {
+    if (!htp_ops_context_set_n_threads(octx, kparams->n_threads)) {
+        return HTP_STATUS_INVAL_PARAMS;
+    }
+
+    const struct htp_copy_transpose_params * tp = &kparams->u.transpose;
+
+    uint32_t tile_start = 0;
+    uint32_t ntiles     = tp->total_tiles;
+
+    if (octx->ctx->mdev.count > 1) {
+        const struct htp_tensor_mdev_range range = htp_tensor_mdev_partition(
+            tp->total_tiles, 1, octx->ctx->mdev.idx, octx->ctx->mdev.count, &octx->ctx->mdev.count_div);
+        tile_start = range.start;
+        ntiles     = range.count;
+    }
+
+    if (ntiles == 0) {
+        return HTP_STATUS_OK;
+    }
+
+    if (kparams->vtcm_size > octx->ctx->vtcm_size) {
+        return HTP_STATUS_VTCM_TOO_SMALL;
+    }
+
+    const uint32_t n_threads = octx->n_threads;
+
+    struct htp_copy_context ct;
+    ct.octx             = octx;
+    ct.kparams          = kparams;
+    ct.tile_start       = tile_start;
+    ct.ntiles           = ntiles;
+    ct.tiles_per_thread = fastdiv(ntiles + n_threads - 1, &octx->n_threads_div);
+
+    uint8_t * vtcm_base = (uint8_t *) octx->ctx->vtcm_base;
+    ct.vtcm_src0 = vtcm_base;
+    ct.vtcm_dst  = vtcm_base + (size_t) n_threads * 2 * tp->in_buf_size;
+
+    work_queue_run(octx->ctx->work_queue, cpy_thread_transpose, &ct, n_threads);
+    return HTP_STATUS_OK;
+}
+
 int op_cpy(struct htp_ops_context * octx) {
    const struct htp_copy_kernel_params * kparams = (const struct htp_copy_kernel_params *) octx->kernel_params;
    int status = HTP_STATUS_OK;
@@ -459,6 +670,9 @@ int op_cpy(struct htp_ops_context * octx) {
        case HTP_COPY_KERNEL_RESHAPE:
            status = cpy_reshape(octx, kparams);
            break;
+        case HTP_COPY_KERNEL_TRANSPOSE:
+            status = cpy_transpose(octx, kparams);
+            break;
        case HTP_COPY_KERNEL_GATHER_ROWS:
            status = htp_gather_rows(octx, octx->src[0], NULL, &kparams->u.gather, kparams->n_threads);
            break;
@@ -15,6 +15,7 @@ enum htp_copy_kernel_type {
    HTP_COPY_KERNEL_RESHAPE            = 4,
    HTP_COPY_KERNEL_SCALAR             = 5,
    HTP_COPY_KERNEL_GATHER_ROWS        = 6,
+    HTP_COPY_KERNEL_TRANSPOSE          = 7,
 };

 struct htp_copy_convert_params {
@@ -32,6 +33,29 @@ struct htp_copy_reshape_params {
    struct fastdiv_values div_ne02_ne01_ne00;
 };

+// Batched 2D transpose of elem_size elements (dim 0 dense in dst, dim 1 dense in src)
+struct htp_copy_transpose_params {
+    uint32_t ne[3];          // dims 0..2
+    uint32_t src_nb[3];      // src strides for dims 0, 2, 3
+    uint32_t dst_nb[3];      // dst strides for dims 1, 2, 3
+    struct fastdiv_values div_nkb;
+    struct fastdiv_values div_n0b;
+    struct fastdiv_values div_ne2;
+    uint32_t blk0;           // dim-0 elements per tile
+    uint32_t blkk;           // dim-1 elements per tile
+    uint32_t n0b;            // tiles along dim 0
+    uint32_t nkb;            // tiles along dim 1
+    uint32_t in_stride;      // VTCM row pitch of staged src tile
+    uint32_t out_stride;     // VTCM row pitch of transposed tile
+    uint32_t in_buf_size;
+    uint32_t out_buf_size;
+    uint32_t total_tiles;
+    uint8_t  elem_size;
+    uint8_t  packed;         // gather straight to dst order
+    uint8_t  log2_m;         // log2(gather lanes per element)
+    uint8_t  log2_r;         // log2(dim-0 elements per vector)
+};
+
 struct htp_copy_kernel_params {
    uint8_t  kernel_type;
    uint8_t  src0_type_size;
@@ -46,6 +70,7 @@ struct htp_copy_kernel_params {
        struct htp_copy_convert_params convert;
        struct htp_copy_reshape_params reshape;
        struct htp_gather_rows_params  gather;
+        struct htp_copy_transpose_params transpose;
    } u;
 };

@@ -116,6 +141,76 @@ static inline void htp_copy_convert_solve_layout(
    htp_copy_convert_vtcm_layout_build(layout, ne00, src_type_size, dst_type_size, blk_rows, n_threads);
 }

+#define HTP_COPY_TRANSPOSE_BLK_BYTES (64 * 1024)
+
+static inline bool htp_copy_transpose_layout_build(struct htp_copy_transpose_params * tp, uint32_t blk_bytes) {
+    const uint32_t ne0  = tp->ne[0];
+    const uint32_t nek  = tp->ne[1];
+    const uint32_t es   = tp->elem_size;
+    const uint32_t epv  = 128 / es;
+
+    uint32_t blk0;
+    uint32_t blkk;
+    if (tp->packed) {
+        // Packed: vector holds epv / ne0 whole dst rows
+        blk0 = ne0;
+        blkk = blk_bytes / (ne0 * es);
+        if (es == 2 && blkk > 16384 / ne0 - 64) {
+            blkk = 16384 / ne0 - 64;
+        }
+        if (blkk > nek) {
+            blkk = nek;
+        }
+        if (blkk == 0) {
+            return false;
+        }
+    } else if (nek <= epv) {
+        // Stage short contiguous dim and expand dim 0
+        blkk = nek;
+        blk0 = blk_bytes / (nek * es);
+        blk0 -= blk0 % epv;
+        if (blk0 > hex_round_up(ne0, epv)) {
+            blk0 = hex_round_up(ne0, epv);
+        }
+        if (blk0 < epv) {
+            return false;
+        }
+    } else {
+        blk0 = hex_round_up(ne0, epv);
+        if (blk0 > 256) {
+            blk0 = 256;
+        }
+        blkk = blk_bytes / (blk0 * es);
+        blkk -= blkk % epv;
+        // Cap f16 blkk to 256 for 16-bit gather offsets
+        const uint32_t max_blkk = (es == 2 ? 512 : 4096) / es;
+        if (blkk > max_blkk) {
+            blkk = max_blkk;
+        }
+        if (blkk > hex_round_up(nek, epv)) {
+            blkk = hex_round_up(nek, epv);
+        }
+        if (blkk < epv) {
+            return false;
+        }
+    }
+
+    tp->blk0         = blk0;
+    tp->blkk         = blkk;
+    tp->n0b          = (ne0 + blk0 - 1) / blk0;
+    tp->nkb          = (nek + blkk - 1) / blkk;
+    // Pad row pitch by 1 element to avoid VTCM bank conflicts
+    const uint32_t pad = (es == 2) ? 2 : 4;
+    tp->in_stride    = blkk * es + pad;
+    tp->out_stride   = blk0 * es;
+    // Extra vector for packed gather overhang
+    tp->in_buf_size  = hex_round_up(blk0 * tp->in_stride + (tp->packed ? 128 : 0), 128);
+    tp->out_buf_size = hex_round_up(blkk * tp->out_stride, 128);
+    tp->log2_m       = es == 2 ? 0 : __builtin_ctz(es) - 2;
+    tp->log2_r       = __builtin_ctz(tp->packed ? ne0 : epv);
+    return true;
+}
+
 #if defined(__cplusplus)
 static_assert(sizeof(struct htp_copy_kernel_params) <= 128, "htp_copy_kernel_params is too large for kernel_params blob");
 #else
@@ -141,43 +141,9 @@ bool dma_queue_push_fallback_2d(dma_queue * q, dma_data ddata, size_t dst_stride
    dma_ring_flush(r1);
    r0->tail = r1->tail;

-    return dma_ring_push_single_2d(r0, ddata, 0, 0, 0, /*nrows=*/ 0);
-}
-
-bool dma_queue_push_fallback_contig(dma_queue * q, dma_data ddata, size_t total) {
-    dma_ring * r0 = q->ring0;
-    dma_ring * r1 = q->ring1;
-
-    if (((r0->push_idx + 1) & r0->idx_mask) == r0->pop_idx) {
-        return false;
-    }
-
-    r1->tail = r0->tail;
-
-    size_t rem_bytes   = total;
-    dma_addr_t cur_dst = ddata.dst;
-    dma_addr_t cur_src = ddata.src;
-
-    while (rem_bytes > 0) {
-        const uint32_t cur_bytes = MIN(rem_bytes, DMA_SAFE_CHUNK_SIZE);
-        dma_data cur_data = dma_make_data(cur_dst, cur_src);
-        if (!dma_ring_push_single_contig(r1, cur_data, cur_bytes)) {
-            dma_ring_flush(r1);
-            dma_ring_push_single_contig(r1, cur_data, cur_bytes);
-        }
-        cur_dst   += cur_bytes;
-        cur_src   += cur_bytes;
-        rem_bytes -= cur_bytes;
-    }
-
-    dma_ring_flush(r1);
-    r0->tail = r1->tail;
-
    return dma_ring_push_single_1d(r0, ddata, /*size=*/ 0);
 }

-#if __HVX_ARCH__ < 75
-
 bool dma_queue_push_fallback_1d(dma_queue * q, dma_data ddata, size_t dst_stride, size_t src_stride, size_t row_size, size_t nrows) {
    dma_ring * r0 = q->ring0;
    dma_ring * r1 = q->ring1;
@@ -188,19 +154,28 @@ bool dma_queue_push_fallback_1d(dma_queue * q, dma_data ddata, size_t dst_stride

    r1->tail = r0->tail;

-    size_t rem_rows    = nrows;
-    dma_addr_t cur_dst = ddata.dst;
-    dma_addr_t cur_src = ddata.src;
+    dma_addr_t row_dst = ddata.dst;
+    dma_addr_t row_src = ddata.src;

-    while (rem_rows > 0) {
-        dma_data cur_data = dma_make_data(cur_dst, cur_src);
-        if (!dma_ring_push_single_1d(r1, cur_data, row_size)) {
-            dma_ring_flush(r1);
-            dma_ring_push_single_1d(r1, cur_data, row_size);
+    for (size_t i = 0; i < nrows; i++) {
+        size_t     rem_bytes = row_size;
+        dma_addr_t cur_dst   = row_dst;
+        dma_addr_t cur_src   = row_src;
+
+        while (rem_bytes > 0) {
+            const uint32_t cur_bytes = MIN(rem_bytes, DMA_SAFE_CHUNK_SIZE);
+            dma_data cur_data = dma_make_data(cur_dst, cur_src);
+            if (!dma_ring_push_single_contig(r1, cur_data, cur_bytes)) {
+                dma_ring_flush(r1);
+                dma_ring_push_single_contig(r1, cur_data, cur_bytes);
+            }
+            cur_dst   += cur_bytes;
+            cur_src   += cur_bytes;
+            rem_bytes -= cur_bytes;
        }
-        cur_dst  += dst_stride;
-        cur_src  += src_stride;
-        rem_rows -= 1;
+
+        row_dst += dst_stride;
+        row_src += src_stride;
    }

    dma_ring_flush(r1);
@@ -208,5 +183,3 @@ bool dma_queue_push_fallback_1d(dma_queue * q, dma_data ddata, size_t dst_stride

    return dma_ring_push_single_1d(r0, ddata, /*size=*/ 0);
 }
-
-#endif
@@ -148,10 +148,7 @@ dma_queue_t dma_queue_alias_init(void * ptr, dma_queue_t main_q);
 void        dma_queue_alias_free(dma_queue_t q);

 bool        dma_queue_push_fallback_2d(dma_queue * q, dma_data ddata, size_t dst_stride, size_t src_stride, size_t row_size, size_t nrows);
-bool        dma_queue_push_fallback_contig(dma_queue * q, dma_data ddata, size_t total);
-#if __HVX_ARCH__ < 75
 bool        dma_queue_push_fallback_1d(dma_queue * q, dma_data ddata, size_t dst_stride, size_t src_stride, size_t row_size, size_t nrows);
-#endif

 // TODO: technically we don't need these and could use Q6_dmstart/wait/etc instead
 // but those do not seem to always compiler properly.
@@ -400,7 +397,7 @@ static inline bool dma_queue_push(dma_queue * q, dma_data ddata, size_t dst_stri
        if (__builtin_expect(total <= DMA_MAX_SIZE_24B, 1)) {
            return dma_ring_push_single_contig(q->ring0, ddata, total);
        }
-        return dma_queue_push_fallback_contig(q, ddata, total);
+        return dma_queue_push_fallback_1d(q, ddata, 0, 0, total, 1);
    }

    // 2. Hot path: Standard strided 2D (10-20% of calls)
@@ -410,14 +407,10 @@ static inline bool dma_queue_push(dma_queue * q, dma_data ddata, size_t dst_stri
    }

    // 3. Cold path: Descriptor chunking fallbacks (< 0.1%)
-#if __HVX_ARCH__ < 75
-    if (row_size <= DMA_MAX_SIZE_16B && (src_stride | dst_stride) <= DMA_MAX_STRIDE_16B) {
+    if ((row_size | src_stride | dst_stride) <= DMA_MAX_2D_ROW_SIZE) {
        return dma_queue_push_fallback_2d(q, ddata, dst_stride, src_stride, row_size, nrows);
    }
    return dma_queue_push_fallback_1d(q, ddata, dst_stride, src_stride, row_size, nrows);
-#else
-    return dma_queue_push_fallback_2d(q, ddata, dst_stride, src_stride, row_size, nrows);
-#endif
 }

 static inline void dma_sync_read(dma_queue * dma_q, void * dst, dma_addr_t src, size_t bytes) {
@@ -0,0 +1,675 @@
+#pragma clang diagnostic ignored "-Wunused-variable"
+#pragma clang diagnostic ignored "-Wunused-function"
+
+#include <HAP_farf.h>
+
+#include <stdint.h>
+#include <string.h>
+#include <math.h>
+
+#include "dma-queue.h"
+#include "hex-common.h"
+#include "hex-profile.h"
+#include "htp-ctx.h"
+#include "htp-tensor.h"
+#include "hvx-utils.h"
+#include "dsv4-hc-ops.h"
+
+// ---------------------------------------------------------------------------
+// DSV4_HC_PRE
+// ---------------------------------------------------------------------------
+
+// hvx_vec_fast_sigmoid_f32_guard_2it with fewer live constants and predicates
+static inline HVX_Vector dsv4_hc_sigmoid_f32(HVX_Vector v, HVX_Vector max_exp, HVX_Vector min_exp) {
+    v = Q6_Vsf_vmin_VsfVsf(Q6_Vsf_vmax_VsfVsf(v, min_exp), max_exp);
+    v = Q6_Vqf32_vmpy_VsfVsf(v, hvx_vec_splat_f32(0.5f * 1.442695022f));
+
+    HVX_Vector in_int = Q6_Vw_equals_Vsf(Q6_Vsf_equals_Vqf32(v));
+    HVX_Vector x      = Q6_Vqf32_vsub_Vqf32Vsf(v, Q6_Vsf_equals_Vw(in_int));
+    HVX_Vector xx     = Q6_Vqf32_vmpy_Vqf32Vqf32(x, x);
+
+    HVX_Vector v1 = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(xx), Q6_V_vsplat_R(FAST_SIGMOID_C2));
+    v1            = Q6_Vqf32_vadd_Vqf32Vsf(v1, Q6_V_vsplat_R(FAST_SIGMOID_LOG2F));
+
+    HVX_Vector v2 = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(x), Q6_V_vsplat_R(FAST_SIGMOID_C1));
+    v2            = Q6_Vqf32_vmpy_Vqf32Vqf32(v2, xx);
+    v2            = Q6_Vqf32_vadd_Vqf32Vqf32(v2, x);
+
+    HVX_Vector v3 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vqf32(v2, v1));
+    v3            = Q6_Vw_vaslacc_VwVwR(v3, in_int, 24);
+
+    HVX_Vector v4 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_Vqf32Vqf32(v2, v1));
+    HVX_Vector v5 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(v3, v4));
+
+    // Newton-Raphson with 2 iterations
+    HVX_Vector r = Q6_Vw_vsub_VwVw(Q6_V_vsplat_R(0x7EEEEBB3), v5);
+    for (int i = 0; i < 2; ++i) {
+        const HVX_Vector rv = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r, v5));
+        r = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_Vqf32Vqf32(Q6_Vqf32_vadd_VsfVsf(r, r), Q6_Vqf32_vmpy_VsfVsf(r, rv)));
+    }
+
+    return Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(v3, r));
+}
+
+struct dsv4_hc_pre_context {
+    struct htp_ops_context * octx;
+    const struct htp_dsv4_hc_pre_kernel_params * kparams;
+    uint32_t token_start;
+    uint32_t n_chunks;
+    uint32_t total_jobs;
+    uint32_t jobs_per_thread;
+    float scale;
+    uint8_t * vtcm;
+    uint32_t dma_block_chunks;
+    uint32_t dma_row_stride;
+    uint32_t dma_buffer_size;
+};
+
+struct dsv4_hc_block {
+    uint32_t it;
+    uint32_t chunk;
+    uint32_t count;
+};
+
+static inline struct dsv4_hc_block dsv4_hc_pre_block_make(
+        const struct dsv4_hc_pre_context * dctx,
+        uint32_t job,
+        uint32_t end) {
+    const uint32_t local_token = fastdiv(job, &dctx->kparams->div_n_chunks);
+    const uint32_t chunk       = job - local_token*dctx->n_chunks;
+    const uint32_t count       = hex_smin(dctx->dma_block_chunks, hex_smin(dctx->n_chunks - chunk, end - job));
+    return (struct dsv4_hc_block) {
+        .it    = dctx->token_start + local_token,
+        .chunk = chunk,
+        .count = count,
+    };
+}
+
+static inline void dsv4_hc_pre_push_plain(
+        dma_queue * dma_q,
+        const struct dsv4_hc_pre_context * dctx,
+        const struct dsv4_hc_block * block,
+        uint8_t * spad) {
+    const struct htp_tensor * x       = dctx->octx->src[0];
+    const struct htp_tensor * weights = dctx->octx->src[1];
+    const uint32_t row_stride         = dctx->dma_row_stride;
+    const uint32_t row_size           = block->count * VLEN;
+
+    dma_queue_push(dma_q, dma_make_data(spad, x->data + (block->it*x->nb[2] + block->chunk*VLEN)), row_stride, x->nb[1], row_size, 4);
+    dma_queue_push(dma_q, dma_make_data(spad + 5*row_stride, weights->data + (block->it*weights->nb[1])), 16, 16, 16, 1);
+}
+
+static inline void dsv4_hc_pre_push_gated(
+        dma_queue * dma_q,
+        const struct dsv4_hc_pre_context * dctx,
+        const struct dsv4_hc_block * block,
+        uint8_t * spad) {
+    const struct htp_tensor * x       = dctx->octx->src[0];
+    const struct htp_tensor * g       = dctx->octx->src[1];
+    const uint32_t row_stride         = dctx->dma_row_stride;
+    const uint32_t row_size           = block->count * VLEN;
+
+    dma_queue_push(dma_q, dma_make_data(spad, x->data + (block->it*x->nb[2] + block->chunk*VLEN)), row_stride, x->nb[1], row_size, 4);
+    dma_queue_push(dma_q, dma_make_data(spad + 4*row_stride, g->data + (block->it*g->nb[2] + block->chunk*VLEN)), row_stride, g->nb[1], row_size, 4);
+}
+
+static __attribute__((noinline)) void dsv4_hc_pre_plain_block_f32(
+        const struct dsv4_hc_pre_context * dctx,
+        const struct dsv4_hc_block * block,
+        const uint8_t * x_spad,
+        uint8_t * d_spad,
+        const float * wb) {
+    const uint32_t row_stride = dctx->dma_row_stride;
+    const HVX_Vector scale_v  = hvx_vec_splat_f32(dctx->scale);
+    const HVX_Vector w0       = hvx_vec_splat_f32(wb[0]);
+    const HVX_Vector w1       = hvx_vec_splat_f32(wb[1]);
+    const HVX_Vector w2       = hvx_vec_splat_f32(wb[2]);
+    const HVX_Vector w3       = hvx_vec_splat_f32(wb[3]);
+
+    for (uint32_t c = 0; c < block->count; ++c) {
+        const uint8_t * xb = x_spad + c*VLEN;
+
+        HVX_Vector x0 = *(const HVX_Vector *) xb;
+        HVX_Vector x1 = *(const HVX_Vector *) (xb + row_stride);
+        HVX_Vector x2 = *(const HVX_Vector *) (xb + 2*row_stride);
+        HVX_Vector x3 = *(const HVX_Vector *) (xb + 3*row_stride);
+
+        HVX_Vector acc = hvx_vec_mul_f32_f32(x0, w0);
+        acc = hvx_vec_add_f32_f32(acc, hvx_vec_mul_f32_f32(x1, w1));
+        acc = hvx_vec_add_f32_f32(acc, hvx_vec_mul_f32_f32(x2, w2));
+        acc = hvx_vec_add_f32_f32(acc, hvx_vec_mul_f32_f32(x3, w3));
+
+        *(HVX_Vector *) (d_spad + c*VLEN) = hvx_vec_mul_f32_f32(acc, scale_v);
+    }
+}
+
+static __attribute__((noinline)) void dsv4_hc_pre_gated_block_f32(
+        const struct dsv4_hc_pre_context * dctx,
+        const struct dsv4_hc_block * block,
+        const uint8_t * x_spad,
+        const uint8_t * g_spad,
+        uint8_t * d_spad) {
+    const uint32_t row_stride = dctx->dma_row_stride;
+    const HVX_Vector scale_v = hvx_vec_splat_f32(dctx->scale);
+    const HVX_Vector max_exp = hvx_vec_splat_f32(87.0f);
+    const HVX_Vector min_exp = hvx_vec_splat_f32(-87.0f);
+
+    for (uint32_t c = 0; c < block->count; ++c) {
+        const uint8_t * xb = x_spad + c*VLEN;
+        const uint8_t * gb = g_spad + c*VLEN;
+
+        HVX_Vector xv = *(const HVX_Vector *) xb;
+        HVX_Vector gv = dsv4_hc_sigmoid_f32(*(const HVX_Vector *) gb, max_exp, min_exp);
+        HVX_Vector acc = hvx_vec_mul_f32_f32(xv, gv);
+
+        xv = *(const HVX_Vector *) (xb + row_stride);
+        gv = dsv4_hc_sigmoid_f32(*(const HVX_Vector *) (gb + row_stride), max_exp, min_exp);
+        acc = hvx_vec_add_f32_f32(acc, hvx_vec_mul_f32_f32(xv, gv));
+
+        xv = *(const HVX_Vector *) (xb + 2*row_stride);
+        gv = dsv4_hc_sigmoid_f32(*(const HVX_Vector *) (gb + 2*row_stride), max_exp, min_exp);
+        acc = hvx_vec_add_f32_f32(acc, hvx_vec_mul_f32_f32(xv, gv));
+
+        xv = *(const HVX_Vector *) (xb + 3*row_stride);
+        gv = dsv4_hc_sigmoid_f32(*(const HVX_Vector *) (gb + 3*row_stride), max_exp, min_exp);
+        acc = hvx_vec_add_f32_f32(acc, hvx_vec_mul_f32_f32(xv, gv));
+
+        *(HVX_Vector *) (d_spad + c*VLEN) = hvx_vec_mul_f32_f32(acc, scale_v);
+    }
+}
+
+static void dsv4_hc_pre_thread_plain_f32(unsigned int nth, unsigned int ith, void * data) {
+    struct dsv4_hc_pre_context * dctx = (struct dsv4_hc_pre_context *) data;
+    struct htp_ops_context *     octx = dctx->octx;
+
+    const struct htp_tensor * dst    = octx->dst;
+    const uint64_t            d_data = dst->data;
+
+    const uint32_t row_stride  = dctx->dma_row_stride;
+    const uint32_t buffer_size = dctx->dma_buffer_size;
+    const uint32_t start       = dctx->jobs_per_thread * ith;
+    const uint32_t end         = hex_smin(start + dctx->jobs_per_thread, dctx->total_jobs);
+    if (start >= end) {
+        return;
+    }
+
+    uint8_t * spad[2] = {
+        dctx->vtcm + 2*buffer_size*ith,
+        dctx->vtcm + 2*buffer_size*ith + buffer_size,
+    };
+
+    dma_queue * dma_q = octx->ctx->dma[ith];
+    uint32_t prefetch_job = start;
+    for (uint32_t i = 0; i < 2 && prefetch_job < end; ++i) {
+        const struct dsv4_hc_block block = dsv4_hc_pre_block_make(dctx, prefetch_job, end);
+        // empty dst writeback keeps pop order dst, x, weights in loop
+        dma_queue_push(dma_q, dma_make_data(d_data, spad[i] + 4*row_stride), block.count*VLEN, block.count*VLEN, 0, 0);
+        dsv4_hc_pre_push_plain(dma_q, dctx, &block, spad[i]);
+        prefetch_job += block.count;
+    }
+
+    struct htp_thread_trace * tr = &octx->ctx->trace[ith];
+
+    uint32_t job  = start;
+    uint32_t curr = 0;
+    while (job < end) {
+        const struct dsv4_hc_block block = dsv4_hc_pre_block_make(dctx, job, end);
+
+        dma_queue_pop(dma_q);
+        dma_queue_pop(dma_q);
+        dma_queue_pop(dma_q);
+
+        const float * wb = (const float *) (spad[curr] + 5*row_stride);
+        uint8_t * d_spad = spad[curr] + 4*row_stride;
+
+        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) block.it);
+        dsv4_hc_pre_plain_block_f32(dctx, &block, spad[curr], d_spad, wb);
+        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) block.it);
+
+        const uint64_t d_dst = d_data + (block.it*dst->nb[1] + block.chunk*VLEN);
+        dma_queue_push(dma_q, dma_make_data(d_dst, d_spad), block.count*VLEN, block.count*VLEN, block.count*VLEN, 1);
+
+        if (prefetch_job < end) {
+            const struct dsv4_hc_block next = dsv4_hc_pre_block_make(dctx, prefetch_job, end);
+            dsv4_hc_pre_push_plain(dma_q, dctx, &next, spad[curr]);
+            prefetch_job += next.count;
+        }
+
+        job += block.count;
+        curr ^= 1;
+    }
+
+    dma_queue_flush(dma_q);
+
+    (void) nth;
+}
+
+static void dsv4_hc_pre_thread_gated_f32(unsigned int nth, unsigned int ith, void * data) {
+    struct dsv4_hc_pre_context * dctx = (struct dsv4_hc_pre_context *) data;
+    struct htp_ops_context *     octx = dctx->octx;
+
+    const struct htp_tensor * dst    = octx->dst;
+    const uint64_t            d_data = dst->data;
+
+    const uint32_t row_stride  = dctx->dma_row_stride;
+    const uint32_t buffer_size = dctx->dma_buffer_size;
+    const uint32_t start       = dctx->jobs_per_thread * ith;
+    const uint32_t end         = hex_smin(start + dctx->jobs_per_thread, dctx->total_jobs);
+    if (start >= end) {
+        return;
+    }
+
+    uint8_t * spad[2] = {
+        dctx->vtcm + 2*buffer_size*ith,
+        dctx->vtcm + 2*buffer_size*ith + buffer_size,
+    };
+
+    dma_queue * dma_q = octx->ctx->dma[ith];
+    uint32_t prefetch_job = start;
+    for (uint32_t i = 0; i < 2 && prefetch_job < end; ++i) {
+        const struct dsv4_hc_block block = dsv4_hc_pre_block_make(dctx, prefetch_job, end);
+        // empty dst writeback keeps pop order dst, x, g in loop
+        dma_queue_push(dma_q, dma_make_data(d_data, spad[i] + 8*row_stride), block.count*VLEN, block.count*VLEN, 0, 0);
+        dsv4_hc_pre_push_gated(dma_q, dctx, &block, spad[i]);
+        prefetch_job += block.count;
+    }
+
+    struct htp_thread_trace * tr = &octx->ctx->trace[ith];
+
+    uint32_t job  = start;
+    uint32_t curr = 0;
+    while (job < end) {
+        const struct dsv4_hc_block block = dsv4_hc_pre_block_make(dctx, job, end);
+
+        dma_queue_pop(dma_q);
+        dma_queue_pop(dma_q);
+        dma_queue_pop(dma_q);
+
+        uint8_t * d_spad = spad[curr] + 8*row_stride;
+
+        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) block.it);
+        dsv4_hc_pre_gated_block_f32(dctx, &block, spad[curr], spad[curr] + 4*row_stride, d_spad);
+        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) block.it);
+
+        const uint64_t d_dst = d_data + (block.it*dst->nb[1] + block.chunk*VLEN);
+        dma_queue_push(dma_q, dma_make_data(d_dst, d_spad), block.count*VLEN, block.count*VLEN, block.count*VLEN, 1);
+
+        if (prefetch_job < end) {
+            const struct dsv4_hc_block next = dsv4_hc_pre_block_make(dctx, prefetch_job, end);
+            dsv4_hc_pre_push_gated(dma_q, dctx, &next, spad[curr]);
+            prefetch_job += next.count;
+        }
+
+        job += block.count;
+        curr ^= 1;
+    }
+
+    dma_queue_flush(dma_q);
+
+    (void) nth;
+}
+
+int op_dsv4_hc_pre(struct htp_ops_context * octx) {
+    const struct htp_tensor * x       = octx->src[0];
+    const struct htp_tensor * weights = octx->src[1];
+    const struct htp_tensor * dst     = octx->dst;
+
+    const struct htp_dsv4_hc_pre_kernel_params * kparams =
+        (const struct htp_dsv4_hc_pre_kernel_params *) octx->kernel_params;
+
+    const uint32_t n_threads = kparams->n_threads;
+    if (!htp_ops_context_set_n_threads_div(octx, n_threads, kparams->div_n_threads)) {
+        return HTP_STATUS_INVAL_PARAMS;
+    }
+
+    uint32_t token_start     = 0;
+    uint32_t n_tokens        = x->ne[2];
+    uint32_t total_jobs      = kparams->total_jobs;
+    uint32_t jobs_per_thread = kparams->jobs_per_thread;
+    if (octx->ctx->mdev.count > 1) {
+        const bool can_split = htp_tensor_mdev_data_aligned(dst) && ((dst->nb[1] & (HTP_TENSOR_MDEV_LINE_SIZE - 1)) == 0);
+        const struct htp_tensor_mdev_range range = htp_tensor_mdev_partition(n_tokens, can_split ? 1 : 0, octx->ctx->mdev.idx, octx->ctx->mdev.count, &octx->ctx->mdev.count_div);
+        token_start     = range.start;
+        n_tokens        = range.count;
+        total_jobs      = n_tokens * kparams->n_chunks;
+        jobs_per_thread = fastdiv(total_jobs + n_threads - 1, &kparams->div_n_threads);
+    }
+
+    if (n_tokens == 0) {
+        return HTP_STATUS_OK;
+    }
+
+    float scale;
+    memcpy(&scale, octx->op_params, sizeof(scale));
+
+    struct dsv4_hc_pre_context dctx = {
+        .octx             = octx,
+        .kparams          = kparams,
+        .token_start      = token_start,
+        .n_chunks         = kparams->n_chunks,
+        .total_jobs       = total_jobs,
+        .jobs_per_thread  = jobs_per_thread,
+        .scale            = scale,
+        .vtcm             = (uint8_t *) octx->ctx->vtcm_base,
+        .dma_block_chunks = kparams->dma_block_chunks,
+        .dma_row_stride   = kparams->dma_row_stride,
+        .dma_buffer_size  = kparams->dma_buffer_size,
+    };
+
+    work_queue_func_t worker = NULL;
+    switch (kparams->kernel_type) {
+        case HTP_DSV4_HC_PRE_KERNEL_PLAIN: worker = dsv4_hc_pre_thread_plain_f32; break;
+        case HTP_DSV4_HC_PRE_KERNEL_GATED: worker = dsv4_hc_pre_thread_gated_f32; break;
+        default: return HTP_STATUS_INVAL_PARAMS;
+    }
+
+    FARF(HIGH, "dsv4-hc-pre: (%ux%ux%u) -> (%ux%u) : ktype %u tokens %u:%u nth %u\n",
+         x->ne[0], x->ne[1], x->ne[2], dst->ne[0], dst->ne[1], kparams->kernel_type,
+         token_start, token_start + n_tokens, n_threads);
+
+    work_queue_run(octx->ctx->work_queue, worker, &dctx, n_threads);
+    return HTP_STATUS_OK;
+}
+
+// ---------------------------------------------------------------------------
+// DSV4_HC_POST
+// ---------------------------------------------------------------------------
+
+struct dsv4_hc_post_context {
+    struct htp_ops_context * octx;
+    const struct htp_dsv4_hc_post_kernel_params * kparams;
+    uint32_t token_start;
+    uint32_t total_jobs;
+    uint32_t jobs_per_thread;
+    uint8_t * vtcm;
+};
+
+static inline struct dsv4_hc_block dsv4_hc_post_block_make(
+        const struct dsv4_hc_post_context * dctx,
+        uint32_t job,
+        uint32_t end,
+        uint32_t block_chunks) {
+    const uint32_t local_token = fastdiv(job, &dctx->kparams->div_n_chunks);
+    const uint32_t chunk       = job - local_token*dctx->kparams->n_chunks;
+    const uint32_t count       = hex_smin(block_chunks, hex_smin(dctx->kparams->n_chunks - chunk, end - job));
+    return (struct dsv4_hc_block) {
+        .it    = dctx->token_start + local_token,
+        .chunk = chunk,
+        .count = count,
+    };
+}
+
+static __attribute__((noinline)) void dsv4_hc_post_identity_block_f32(
+        const uint8_t * xb,
+        const uint8_t * rb,
+        size_t r_stride,
+        uint8_t * db,
+        size_t d_stride,
+        uint32_t count,
+        const float * p) {
+    for (uint32_t id = 0; id < 4; ++id) {
+        const HVX_Vector pv = hvx_vec_splat_f32(p[id]);
+        const uint8_t *  rr = rb + id*r_stride;
+        uint8_t *        dd = db + id*d_stride;
+
+        for (uint32_t i = 0; i < count; ++i) {
+            const HVX_Vector xv = *(const HVX_Vector *) (xb + i*VLEN);
+            const HVX_Vector rv = *(const HVX_Vector *) (rr + i*VLEN);
+            *(HVX_Vector *) (dd + i*VLEN) = hvx_vec_add_f32_f32(hvx_vec_mul_f32_f32(xv, pv), rv);
+        }
+    }
+}
+
+static __attribute__((noinline)) void dsv4_hc_post_comb_block_f32(
+        const uint8_t * xb,
+        const uint8_t * rb,
+        size_t r_stride,
+        uint8_t * db,
+        size_t d_stride,
+        uint32_t count,
+        const float * p,
+        const float * c) {
+    for (uint32_t id = 0; id < 4; ++id) {
+        const HVX_Vector pv = hvx_vec_splat_f32(p[id]);
+        const HVX_Vector c0 = hvx_vec_splat_f32(c[id]);
+        const HVX_Vector c1 = hvx_vec_splat_f32(c[4 + id]);
+        const HVX_Vector c2 = hvx_vec_splat_f32(c[8 + id]);
+        const HVX_Vector c3 = hvx_vec_splat_f32(c[12 + id]);
+        uint8_t * dd = db + id*d_stride;
+
+        for (uint32_t i = 0; i < count; ++i) {
+            const uint8_t * r = rb + i*VLEN;
+            HVX_Vector acc = hvx_vec_mul_f32_f32(*(const HVX_Vector *) (xb + i*VLEN), pv);
+            acc = hvx_vec_add_f32_f32(acc, hvx_vec_mul_f32_f32(*(const HVX_Vector *) r, c0));
+            acc = hvx_vec_add_f32_f32(acc, hvx_vec_mul_f32_f32(*(const HVX_Vector *) (r + r_stride), c1));
+            acc = hvx_vec_add_f32_f32(acc, hvx_vec_mul_f32_f32(*(const HVX_Vector *) (r + 2*r_stride), c2));
+            acc = hvx_vec_add_f32_f32(acc, hvx_vec_mul_f32_f32(*(const HVX_Vector *) (r + 3*r_stride), c3));
+            *(HVX_Vector *) (dd + i*VLEN) = acc;
+        }
+    }
+}
+
+static inline void dsv4_hc_post_push_identity(
+        dma_queue * dma_q,
+        const struct dsv4_hc_post_context * dctx,
+        const struct dsv4_hc_block * block,
+        uint8_t * spad) {
+    const struct htp_tensor * x        = dctx->octx->src[0];
+    const struct htp_tensor * residual = dctx->octx->src[1];
+    const struct htp_tensor * post     = dctx->octx->src[2];
+    const uint32_t row_stride          = dctx->kparams->dma_row_stride;
+    const uint32_t row_size            = block->count * VLEN;
+
+    dma_queue_push(dma_q, dma_make_data(spad, residual->data + (block->it*residual->nb[2] + block->chunk*VLEN)),
+                   row_stride, residual->nb[1], row_size, 4);
+    dma_queue_push(dma_q, dma_make_data(spad + 4*row_stride, x->data + (block->it*x->nb[1] + block->chunk*VLEN)),
+                   row_stride, row_size, row_size, 1);
+    dma_queue_push(dma_q, dma_make_data(spad + 9*row_stride, post->data + (block->it*post->nb[1])),
+                   16, 16, 16, 1);
+}
+
+static inline void dsv4_hc_post_push_comb(
+        dma_queue * dma_q,
+        const struct dsv4_hc_post_context * dctx,
+        const struct dsv4_hc_block * block,
+        uint8_t * spad) {
+    const struct htp_tensor * comb = dctx->octx->src[3];
+    const uint32_t row_stride      = dctx->kparams->dma_row_stride;
+
+    dsv4_hc_post_push_identity(dma_q, dctx, block, spad);
+    dma_queue_push(dma_q, dma_make_data(spad + 9*row_stride + 64, comb->data + (block->it*comb->nb[2])),
+                   16, comb->nb[1], 16, 4);
+}
+
+static void dsv4_hc_post_thread_identity_f32(unsigned int nth, unsigned int ith, void * data) {
+    struct dsv4_hc_post_context * dctx = (struct dsv4_hc_post_context *) data;
+    struct htp_ops_context *      octx = dctx->octx;
+
+    const struct htp_tensor * dst    = octx->dst;
+    const uint64_t            d_data = dst->data;
+
+    const uint32_t block_chunks = dctx->kparams->dma_block_chunks;
+    const uint32_t row_stride   = dctx->kparams->dma_row_stride;
+    const uint32_t buffer_size  = dctx->kparams->dma_buffer_size;
+    const uint32_t start        = dctx->jobs_per_thread * ith;
+    const uint32_t end          = hex_smin(start + dctx->jobs_per_thread, dctx->total_jobs);
+    if (start >= end) {
+        return;
+    }
+
+    uint8_t * spad[2] = {
+        dctx->vtcm + 2*buffer_size*ith,
+        dctx->vtcm + 2*buffer_size*ith + buffer_size,
+    };
+
+    dma_queue * dma_q = octx->ctx->dma[ith];
+    uint32_t prefetch_job = start;
+    for (uint32_t i = 0; i < 2 && prefetch_job < end; ++i) {
+        const struct dsv4_hc_block block = dsv4_hc_post_block_make(dctx, prefetch_job, end, block_chunks);
+        // empty dst writeback keeps pop order dst, residual, x, post in loop
+        dma_queue_push(dma_q, dma_make_data(d_data, spad[i] + 5*row_stride), dst->nb[1], row_stride, 0, 0);
+        dsv4_hc_post_push_identity(dma_q, dctx, &block, spad[i]);
+        prefetch_job += block.count;
+    }
+
+    struct htp_thread_trace * tr = &octx->ctx->trace[ith];
+
+    uint32_t job  = start;
+    uint32_t curr = 0;
+    while (job < end) {
+        const struct dsv4_hc_block block = dsv4_hc_post_block_make(dctx, job, end, block_chunks);
+
+        dma_queue_pop(dma_q);
+        dma_queue_pop(dma_q);
+        dma_queue_pop(dma_q);
+        dma_queue_pop(dma_q);
+
+        const float * p      = (const float *) (spad[curr] + 9*row_stride);
+        uint8_t *     d_spad = spad[curr] + 5*row_stride;
+
+        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) block.it);
+        dsv4_hc_post_identity_block_f32(spad[curr] + 4*row_stride, spad[curr], row_stride, d_spad, row_stride, block.count, p);
+        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) block.it);
+
+        const uint64_t d_dst = d_data + (block.it*dst->nb[2] + block.chunk*VLEN);
+        dma_queue_push(dma_q, dma_make_data(d_dst, d_spad), dst->nb[1], row_stride, block.count*VLEN, 4);
+
+        if (prefetch_job < end) {
+            const struct dsv4_hc_block next = dsv4_hc_post_block_make(dctx, prefetch_job, end, block_chunks);
+            dsv4_hc_post_push_identity(dma_q, dctx, &next, spad[curr]);
+            prefetch_job += next.count;
+        }
+
+        job += block.count;
+        curr ^= 1;
+    }
+
+    dma_queue_flush(dma_q);
+
+    (void) nth;
+}
+
+static void dsv4_hc_post_thread_comb_f32(unsigned int nth, unsigned int ith, void * data) {
+    struct dsv4_hc_post_context * dctx = (struct dsv4_hc_post_context *) data;
+    struct htp_ops_context *      octx = dctx->octx;
+
+    const struct htp_tensor * dst    = octx->dst;
+    const uint64_t            d_data = dst->data;
+
+    const uint32_t block_chunks = dctx->kparams->dma_block_chunks;
+    const uint32_t row_stride   = dctx->kparams->dma_row_stride;
+    const uint32_t buffer_size  = dctx->kparams->dma_buffer_size;
+    const uint32_t start        = dctx->jobs_per_thread * ith;
+    const uint32_t end          = hex_smin(start + dctx->jobs_per_thread, dctx->total_jobs);
+    if (start >= end) {
+        return;
+    }
+
+    uint8_t * spad[2] = {
+        dctx->vtcm + 2*buffer_size*ith,
+        dctx->vtcm + 2*buffer_size*ith + buffer_size,
+    };
+
+    dma_queue * dma_q = octx->ctx->dma[ith];
+    uint32_t prefetch_job = start;
+    for (uint32_t i = 0; i < 2 && prefetch_job < end; ++i) {
+        const struct dsv4_hc_block block = dsv4_hc_post_block_make(dctx, prefetch_job, end, block_chunks);
+        // empty dst writeback keeps pop order dst, residual, x, post, comb in loop
+        dma_queue_push(dma_q, dma_make_data(d_data, spad[i] + 5*row_stride), dst->nb[1], row_stride, 0, 0);
+        dsv4_hc_post_push_comb(dma_q, dctx, &block, spad[i]);
+        prefetch_job += block.count;
+    }
+
+    struct htp_thread_trace * tr = &octx->ctx->trace[ith];
+
+    uint32_t job  = start;
+    uint32_t curr = 0;
+    while (job < end) {
+        const struct dsv4_hc_block block = dsv4_hc_post_block_make(dctx, job, end, block_chunks);
+
+        dma_queue_pop(dma_q);
+        dma_queue_pop(dma_q);
+        dma_queue_pop(dma_q);
+        dma_queue_pop(dma_q);
+        dma_queue_pop(dma_q);
+
+        const float * p      = (const float *) (spad[curr] + 9*row_stride);
+        const float * c      = (const float *) (spad[curr] + 9*row_stride + 64);
+        uint8_t *     d_spad = spad[curr] + 5*row_stride;
+
+        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) block.it);
+        dsv4_hc_post_comb_block_f32(spad[curr] + 4*row_stride, spad[curr], row_stride, d_spad, row_stride, block.count, p, c);
+        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) block.it);
+
+        const uint64_t d_dst = d_data + (block.it*dst->nb[2] + block.chunk*VLEN);
+        dma_queue_push(dma_q, dma_make_data(d_dst, d_spad), dst->nb[1], row_stride, block.count*VLEN, 4);
+
+        if (prefetch_job < end) {
+            const struct dsv4_hc_block next = dsv4_hc_post_block_make(dctx, prefetch_job, end, block_chunks);
+            dsv4_hc_post_push_comb(dma_q, dctx, &next, spad[curr]);
+            prefetch_job += next.count;
+        }
+
+        job += block.count;
+        curr ^= 1;
+    }
+
+    dma_queue_flush(dma_q);
+
+    (void) nth;
+}
+
+int op_dsv4_hc_post(struct htp_ops_context * octx) {
+    const struct htp_tensor * x        = octx->src[0];
+    const struct htp_tensor * residual = octx->src[1];
+    const struct htp_tensor * dst      = octx->dst;
+
+    const struct htp_dsv4_hc_post_kernel_params * kparams =
+        (const struct htp_dsv4_hc_post_kernel_params *) octx->kernel_params;
+
+    const uint32_t n_threads = kparams->n_threads;
+    if (!htp_ops_context_set_n_threads_div(octx, n_threads, kparams->div_n_threads)) {
+        return HTP_STATUS_INVAL_PARAMS;
+    }
+
+    uint32_t token_start     = 0;
+    uint32_t n_tokens        = x->ne[1];
+    uint32_t total_jobs      = kparams->total_jobs;
+    uint32_t jobs_per_thread = kparams->jobs_per_thread;
+    if (octx->ctx->mdev.count > 1) {
+        const bool can_split = htp_tensor_mdev_data_aligned(dst) && ((dst->nb[2] & (HTP_TENSOR_MDEV_LINE_SIZE - 1)) == 0);
+        const struct htp_tensor_mdev_range range = htp_tensor_mdev_partition(n_tokens, can_split ? 1 : 0, octx->ctx->mdev.idx, octx->ctx->mdev.count, &octx->ctx->mdev.count_div);
+        token_start     = range.start;
+        n_tokens        = range.count;
+        total_jobs      = n_tokens * kparams->n_chunks;
+        jobs_per_thread = fastdiv(total_jobs + n_threads - 1, &kparams->div_n_threads);
+    }
+
+    if (n_tokens == 0) {
+        return HTP_STATUS_OK;
+    }
+
+    struct dsv4_hc_post_context dctx = {
+        .octx            = octx,
+        .kparams         = kparams,
+        .token_start     = token_start,
+        .total_jobs      = total_jobs,
+        .jobs_per_thread = jobs_per_thread,
+        .vtcm            = (uint8_t *) octx->ctx->vtcm_base,
+    };
+
+    work_queue_func_t worker = NULL;
+    switch (kparams->kernel_type) {
+        case HTP_DSV4_HC_POST_KERNEL_IDENTITY: worker = dsv4_hc_post_thread_identity_f32; break;
+        case HTP_DSV4_HC_POST_KERNEL_COMB:     worker = dsv4_hc_post_thread_comb_f32;     break;
+        default: return HTP_STATUS_INVAL_PARAMS;
+    }
+
+    FARF(HIGH, "dsv4-hc-post: (%ux%u) (%ux%ux%u) -> (%ux%ux%u) : ktype %u tokens %u:%u nth %u\n",
+         x->ne[0], x->ne[1], residual->ne[0], residual->ne[1], residual->ne[2],
+         dst->ne[0], dst->ne[1], dst->ne[2], kparams->kernel_type,
+         token_start, token_start + n_tokens, n_threads);
+
+    work_queue_run(octx->ctx->work_queue, worker, &dctx, n_threads);
+    return HTP_STATUS_OK;
+}
@@ -0,0 +1,77 @@
+#ifndef HTP_DSV4_HC_OPS_H
+#define HTP_DSV4_HC_OPS_H
+
+#include <stdint.h>
+
+#include "hex-fastdiv.h"
+#include "htp-ops.h"
+
+#define HTP_DSV4_HC_CHUNK_ELEMS 32
+#define HTP_DSV4_HC_MAX_CHUNKS 128
+#define HTP_DSV4_HC_PRE_GATED_ROWS 9
+#define HTP_DSV4_HC_PRE_PLAIN_ROWS 6
+
+enum htp_dsv4_hc_pre_kernel_type {
+    HTP_DSV4_HC_PRE_KERNEL_PLAIN = 0,
+    HTP_DSV4_HC_PRE_KERNEL_GATED = 1,
+};
+
+struct htp_dsv4_hc_pre_kernel_params {
+    uint32_t kernel_type;
+    uint32_t n_threads;
+    uint32_t n_chunks;
+    uint32_t total_jobs;
+    uint32_t jobs_per_thread;
+
+    uint32_t dma_block_chunks;
+    uint32_t dma_row_stride;
+    uint32_t dma_buffer_size;
+    uint32_t vtcm_size;
+
+    struct fastdiv_values div_n_chunks;
+    struct fastdiv_values div_n_threads;
+};
+
+#if defined(__cplusplus)
+static_assert(sizeof(struct htp_dsv4_hc_pre_kernel_params) <= 128, "htp_dsv4_hc_pre_kernel_params is too large for kernel_params blob");
+#else
+_Static_assert(sizeof(struct htp_dsv4_hc_pre_kernel_params) <= 128, "htp_dsv4_hc_pre_kernel_params is too large for kernel_params blob");
+#endif
+
+// dma buffer holds residual rows 0..3, x at row 4, dst rows 5..8, post/comb at row 9
+#define HTP_DSV4_HC_POST_ROWS 10
+
+enum htp_dsv4_hc_post_kernel_type {
+    HTP_DSV4_HC_POST_KERNEL_IDENTITY = 0,
+    HTP_DSV4_HC_POST_KERNEL_COMB     = 1,
+};
+
+struct htp_dsv4_hc_post_kernel_params {
+    uint32_t kernel_type;
+    uint32_t n_threads;
+    uint32_t n_chunks;
+    uint32_t total_jobs;
+    uint32_t jobs_per_thread;
+    uint32_t has_comb;
+
+    uint32_t dma_block_chunks;
+    uint32_t dma_row_stride;
+    uint32_t dma_buffer_size;
+    uint32_t vtcm_size;
+
+    struct fastdiv_values div_n_chunks;
+    struct fastdiv_values div_n_threads;
+};
+
+#if defined(__cplusplus)
+static_assert(sizeof(struct htp_dsv4_hc_post_kernel_params) <= 128, "htp_dsv4_hc_post_kernel_params is too large for kernel_params blob");
+#else
+_Static_assert(sizeof(struct htp_dsv4_hc_post_kernel_params) <= 128, "htp_dsv4_hc_post_kernel_params is too large for kernel_params blob");
+#endif
+
+struct htp_ops_context;
+
+int op_dsv4_hc_pre(struct htp_ops_context * octx);
+int op_dsv4_hc_post(struct htp_ops_context * octx);
+
+#endif // HTP_DSV4_HC_OPS_H
@@ -1456,9 +1456,10 @@ static inline void gdn_dma_push_chunk_gb(
    const dma_addr_t g_dma    = g->data + (uint64_t) iv3 * g->nb[3] + (uint64_t) t_chunk * g->nb[2] + (uint64_t) iv1 * g->nb[1];
    const dma_addr_t beta_dma = beta->data + (uint64_t) iv3 * beta->nb[3] + (uint64_t) t_chunk * beta->nb[2] + (uint64_t) iv1 * beta->nb[1];
    const uint32_t row_bytes  = n_batch * sizeof(float);
+    const uint32_t dst_stride = (n_batch > 1) ? row_bytes + sizeof(float) : row_bytes;

-    dma_queue_push(dma_q, dma_make_data(vtcm_g_raw, g_dma), row_bytes, g->nb[2], row_bytes, chunk_size);
-    dma_queue_push(dma_q, dma_make_data(vtcm_b_raw, beta_dma), row_bytes, beta->nb[2], row_bytes, chunk_size);
+    dma_queue_push(dma_q, dma_make_data(vtcm_g_raw, g_dma), dst_stride, g->nb[2], row_bytes, chunk_size);
+    dma_queue_push(dma_q, dma_make_data(vtcm_b_raw, beta_dma), dst_stride, beta->nb[2], row_bytes, chunk_size);
 }

 static inline void gdn_pack_s_col_tiles(
@@ -1707,19 +1708,16 @@ static void gdn_hvx_phase1a_worker(unsigned int n, unsigned int i, void * data)
        hvx_vmem(head->b_f32[curr_buf] + 0)  = hvx_vmem(bctx->vtcm_b_raw + 0);
        hvx_vmem(head->b_f32[curr_buf] + 32) = hvx_vmem(bctx->vtcm_b_raw + 32);
    } else {
-        int32_t offsets[32] __attribute__((aligned(128)));
-        for (int k = 0; k < 32; ++k) {
-            offsets[k] = k * n_batch * sizeof(float);
-        }
-        HVX_Vector vv = *(const HVX_Vector *) offsets;
+        const uint32_t vtcm_stride = (n_batch + 1) * sizeof(float);
+        const HVX_Vector vv = hvx_vec_gather_offsets_w(vtcm_stride);
        const size_t rt_g = (size_t) ((const uint8_t *) bctx->vtcm_g_raw + i * sizeof(float));
        const size_t rt_b = (size_t) ((const uint8_t *) bctx->vtcm_b_raw + i * sizeof(float));
-        const size_t mu   = 64 * n_batch * sizeof(float);
+        const size_t mu   = 64 * vtcm_stride;

        Q6_vgather_ARMVw((HVX_Vector *) (head->g_f32[curr_buf] + 0),  rt_g, mu, vv);
-        Q6_vgather_ARMVw((HVX_Vector *) (head->g_f32[curr_buf] + 32), rt_g + 32 * n_batch * sizeof(float), mu, vv);
+        Q6_vgather_ARMVw((HVX_Vector *) (head->g_f32[curr_buf] + 32), rt_g + 32 * vtcm_stride, mu, vv);
        Q6_vgather_ARMVw((HVX_Vector *) (head->b_f32[curr_buf] + 0),  rt_b, mu, vv);
-        Q6_vgather_ARMVw((HVX_Vector *) (head->b_f32[curr_buf] + 32), rt_b + 32 * n_batch * sizeof(float), mu, vv);
+        Q6_vgather_ARMVw((HVX_Vector *) (head->b_f32[curr_buf] + 32), rt_b + 32 * vtcm_stride, mu, vv);
    }

    const uint32_t t_chunk = bctx->c * 64;
@@ -204,7 +204,7 @@ static inline void htp_gdn_hmx_vtcm_layout_build(
    VTCM_LAYOUT_ALLOC_OPTIONAL(off, off_g_f32[1], bh * dma_scalar_sz, pipeline);
    VTCM_LAYOUT_ALLOC(off, off_b_f32[0], bh * dma_scalar_sz);
    VTCM_LAYOUT_ALLOC_OPTIONAL(off, off_b_f32[1], bh * dma_scalar_sz, pipeline);
-    const size_t raw_gb_sz = hex_round_up(bh * chunk_size * sizeof(float), 128);
+    const size_t raw_gb_sz = hex_round_up((bh + 1) * chunk_size * sizeof(float), 128);
    VTCM_LAYOUT_ALLOC(off, off_g_raw[0], raw_gb_sz);
    VTCM_LAYOUT_ALLOC_OPTIONAL(off, off_g_raw[1], raw_gb_sz, pipeline);
    VTCM_LAYOUT_ALLOC(off, off_b_raw[0], raw_gb_sz);
@@ -186,53 +186,6 @@ static void swiglu_clamp_f32(const float * restrict src0,
 static const float GELU_COEF_A     = 0.044715f;
 static const float SQRT_2_OVER_PI  = 0.79788456080286535587989211986876f;

-static inline HVX_Vector hvx_vec_fast_sigmoid_f32_2it(HVX_Vector v) {
-    v = Q6_Vqf32_vmpy_VsfVsf(v, Q6_V_vsplat_R(FAST_SIGMOID_LOG2F));
-    v = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(v), Q6_V_vsplat_R(FAST_SIGMOID_C3));
-
-    HVX_Vector in_int = hvx_vec_truncate_f32(Q6_Vsf_equals_Vqf32(v));
-    HVX_Vector x      = Q6_Vqf32_vsub_Vqf32Vsf(v, Q6_Vsf_equals_Vw(in_int));
-    HVX_Vector xx     = Q6_Vqf32_vmpy_Vqf32Vqf32(x, x);
-
-    HVX_Vector v1 = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(xx), Q6_V_vsplat_R(FAST_SIGMOID_C2));
-    v1            = Q6_Vqf32_vadd_Vqf32Vsf(v1, Q6_V_vsplat_R(FAST_SIGMOID_LOG2F));
-
-    HVX_Vector v2 = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(x), Q6_V_vsplat_R(FAST_SIGMOID_C1));
-    v2            = Q6_Vqf32_vmpy_Vqf32Vqf32(v2, xx);
-    v2            = Q6_Vqf32_vadd_Vqf32Vqf32(v2, x);
-
-    HVX_Vector v3 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vqf32(v2, v1));
-    v3            = Q6_Vw_vaslacc_VwVwR(v3, in_int, 24);
-
-    HVX_Vector v4 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_Vqf32Vqf32(v2, v1));
-    HVX_Vector v5 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(v3, v4));
-
-    // Newton-Raphson with 2 iterations
-    HVX_Vector two_sf = hvx_vec_splat_f32(2.0f);
-    HVX_Vector i_sf   = Q6_Vw_vsub_VwVw(Q6_V_vsplat_R(0x7EEEEBB3), v5);
-    HVX_Vector r_qf   = Q6_Vqf32_vmpy_VsfVsf(
-        i_sf, Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(two_sf, Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(i_sf, v5)))));
-    r_qf = Q6_Vqf32_vmpy_Vqf32Vqf32(
-        r_qf, Q6_Vqf32_vsub_VsfVsf(two_sf, Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(r_qf), v5))));
-    HVX_Vector res = Q6_Vsf_equals_Vqf32(r_qf);
-
-    res = Q6_Vqf32_vmpy_VsfVsf(v3, res);
-
-    return Q6_Vsf_equals_Vqf32(res);
-}
-
-static inline HVX_Vector hvx_vec_fast_sigmoid_f32_guard_2it(HVX_Vector v,
-                                                            HVX_Vector one,
-                                                            HVX_Vector max_exp,
-                                                            HVX_Vector min_exp) {
-    const HVX_VectorPred pred_max = Q6_Q_vcmp_gt_VsfVsf(max_exp, v);
-    const HVX_VectorPred pred_min = Q6_Q_vcmp_gt_VsfVsf(v, min_exp);
-
-    HVX_Vector out = hvx_vec_fast_sigmoid_f32_2it(v);
-    out            = Q6_V_vmux_QVV(pred_max, out, one);
-    return Q6_V_vmux_QVV(pred_min, out, Q6_V_vzero());
-}
-
 static inline void hvx_geglu_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) {
    assert((unsigned long) dst  % 128 == 0);
    assert((unsigned long) src0 % 128 == 0);
@@ -131,21 +131,34 @@ static inline void htp_ops_context_reset(struct htp_ops_context * octx, struct h
    octx->n_threads_div = ctx->n_threads_div;
 }

-static inline bool htp_ops_context_set_n_threads(struct htp_ops_context * octx, uint32_t n_threads) {
+static inline bool htp_ops_context_set_n_threads_div(
+        struct htp_ops_context * octx,
+        uint32_t n_threads,
+        struct fastdiv_values n_threads_div) {
    if (n_threads == 0 || n_threads > octx->ctx->n_threads) {
        return false;
    }

-    if (n_threads != octx->n_threads) {
-        octx->n_threads = n_threads;
-        octx->n_threads_div = n_threads == octx->ctx->n_threads
-            ? octx->ctx->n_threads_div
-            : init_fastdiv_values(n_threads);
-    }
+    octx->n_threads     = n_threads;
+    octx->n_threads_div = n_threads_div;

    return true;
 }

+static inline bool htp_ops_context_set_n_threads(struct htp_ops_context * octx, uint32_t n_threads) {
+    if (n_threads == 0 || n_threads > octx->ctx->n_threads) {
+        return false;
+    }
+    if (n_threads == octx->n_threads) {
+        return true;
+    }
+
+    const struct fastdiv_values n_threads_div = n_threads == octx->ctx->n_threads
+        ? octx->ctx->n_threads_div
+        : init_fastdiv_values(n_threads);
+    return htp_ops_context_set_n_threads_div(octx, n_threads, n_threads_div);
+}
+
 static inline void htp_ops_context_set_status(struct htp_ops_context * octx, int status) {
    if (status > HTP_STATUS_OK && octx->status == HTP_STATUS_OK) {
        octx->status = status;
@@ -174,6 +187,7 @@ int op_argsort(struct htp_ops_context * octx);
 int op_top_k(struct htp_ops_context * octx);
 int op_ssm_conv(struct htp_ops_context * octx);
 int op_cumsum(struct htp_ops_context * octx);
+int op_top_k_mask(struct htp_ops_context * octx);
 int op_fill(struct htp_ops_context * octx);
 int op_concat(struct htp_ops_context * octx);
 int op_diag(struct htp_ops_context * octx);
@@ -188,5 +202,7 @@ int op_pool_2d(struct htp_ops_context * octx);
 int op_pool_1d(struct htp_ops_context * octx);
 int op_allreduce(struct htp_ops_context * octx);
 int op_roll(struct htp_ops_context * octx);
+int op_upscale(struct htp_ops_context * octx);
+int op_lightning_indexer(struct htp_ops_context * octx);

 #endif /* HTP_CTX_H */
--- a/Show More
+++ b/Show More