Extract ModelCapabilityIcons (canonical Tools/Reasoning/Vision/Video/Audio
order) out of ModelId and reuse it there, add the shared DialogConfirmDownload
for destructive download actions, the discover org avatar with dark-mode
inversion and the thin download progress bar, and rework ModelId badges to take
thinking/tool-use support directly.
Assisted-by: pi:GLM-5.3-Flash
Add an allowHtml prop to MarkdownContent: raw HTML found in the markdown is
rendered after DOMPurify sanitization instead of being escaped as literal
text. Default stays escaped.
Assisted-by: pi:GLM-5.3-Flash
Track HuggingFace downloads end to end: the server download/cancel endpoints,
a status manager fed by the /models/sse download progress events, and a
models-discover store holding the catalog and detail state for the discover
view. Downloaded and in-flight entries are excluded from the loadable model
list.
Assisted-by: pi:GLM-5.3-Flash
Replace the raw runtime-memory estimate with the app's compatibility check:
the smallest real Mac memory tier that fits a model file, budgeted as
RAM x 0.75 minus fixed overhead with headroom on the file size. The constants
move to lib; the unused runtime-memory estimate is dropped. browser-info's
OS detection is exported for reuse.
Assisted-by: pi:GLM-5.3-Flash
Add HuggingFaceService and its constants/enums/types: GGUF repo search, file
tree and model detail fetching, quant/sidecar filename analysis, shard-set
collapsing and the llama.app catalog feed, plus an orgOf() helper on the model
name utils.
Assisted-by: pi:GLM-5.3-Flash
Extend the shared model id parser with sidecar tokens (draft variants and
auxiliary imatrix/mmproj files), weight-file and custom-quant regexes, and add
the tools capability to ModelCapabilities; the selector option row picks it up
from the model's declared capabilities.
Assisted-by: pi:GLM-5.3-Flash
The download monitor thread acquires the mutex on its way out, so joining
it while holding the lock in server_models::remove deadlocks once the
status has flipped to DOWNLOADED. Join outside the lock, same pattern as
load_models().
Assisted-by: pi:zai-org/GLM-5.3
A Q4_0-mtp style tag now resolves the sidecar file when no model file matches it, so a solo draft or mmproj download actually pulls the file. Cached sidecar files list as their own entries so the state survives a restart, and removing such a tag deletes only the sidecar.
Assisted-by: pi:zai-org/GLM-5.3
* context : do not re-reserve the scheduler when toggling causal_attn
`llama_context::set_causal_attn()` marks the scheduler to do a full re-reserve on every change of the flag. For vision inputs, this flag is flipped twice around each non-causal image chunk for Gemma models, resulting in two expensive `sched_reserve()` passes per image. This is especially slow for multi-image or video inputs.
The cost of a re-reserve scales with context and ubatch configurations, so larger settings pay more per image (see table below).
The re-reserve is unnecessary in this case because `causal_attn` only changes the values written to KQ mask, not tensor shapes or any other buffer sizes.
Note: `causal_attn` is a graph reuse key (`llm_graph_params` via `cparams`), so a new graph is built regardless of `sched_need_reserve`, so this doesn't change the graph rebuilding behaviour.
llama-server with gemma-4-26B-A4B Q4_0 + BF16 mmproj, 130-token images,
cache_prompt=false, prompt_ms median of 3 (before -> after):
| images | config | H200 before -> after | RTX 4090 before -> after |
|-|-|-|-|
| 1 | `-c 8192 -ub 512` | 134 -> 105 ms (1.27×) | 201 -> 119 ms (1.69×) |
| 24 | `-c 8192 -ub 512` | 2278 -> 1562 ms (1.46×) | 3559 -> 1748 ms (2.04×) |
| 24 | `-c 32768 -ub 2048` | 5379 -> 1584 ms (3.40×) | 13377 -> 1759 ms (7.61×) |
Generated output remains identical before and after.
* qwen4exp : make the indexer bias shape independent of causal_attn
The block/cell bias path was selected on cparams.causal_attn, so the
causal and non-causal graphs differed in tensor shapes and ops. With the
re-reserve removed (previous commit), a runtime flip resulted in
reallocating the compute buffers, which would fail under
GGML_SCHED_NO_REALLOC.
This commit selects the block path from the mask shape only, independent
of causal_attn. causal_attn is instead passed to set_input_qsa.
causal_attn is fixed per graph as it's part of the reuse key. Causal
values are unchanged. Non-causal values now follow the reference rule,
where every visible block competes on score and only unpooled cells are
always selected.
* context : state the causal_attn shape rule in the comment
* cont : add TODOs
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* vulkan: read the batch stride of an in place src0 from nb[2]
A dim01 contiguous tensor can still be a view whose batches are
strided by more than ne[1] rows, the first rows of a KV cache for
example. Both the mat-vec and the matrix paths read such a tensor in
place but passed ne00*ne01 as the batch stride, so every head past
the first read the wrong rows. The same applies to src1. The stride
now comes from nb[2] whenever the tensor is used in place; the value
is unchanged for a contiguous tensor.
test-backend-ops gets an m_v parameter on test_mul_mat, the number of
rows of a in memory, and two cases at the shapes of a decoder self
attention over a cache.
* vulkan: size the in place A and B ranges by their strided extent
The matrix path bound src0 and src1 to the shader with a range of
elements times type size, which ends before the batches of a strided
view. Pipelines with bounded access read zero past that range, so the
same view that the mat-vec path already handles gave wrong results
on Intel and on NVIDIA without coopmat2. The range now comes from
ggml_nbytes when the tensor is read in place.
* vulkan: address review from jeffbolznv
Bind the in place A and B of the matrix path with ggml_vk_subbuffer,
which spans to the end of the buffer, so a strided view is in range
without computing its extent.
mul_mat_id reads the batch stride of an in place src0 and src1 with
the same helper as mul_mat. test_mul_mat_id gets an m_v parameter,
the number of rows of as in memory, and a case whose experts are
strided by more rows than it uses.
* vulkan: read the batch stride of an in place src0 in mul_mat_vec_id
The single token path of mul_mat_id passed ne00*ne01 as the batch
stride of A, so a strided expert view read the wrong rows. The stride
now comes from ggml_vk_batch_stride like the other three paths, and
src1 follows the same rule.
test_mul_mat_id gets a single token case over the strided view.
* vulkan: address review from jeffbolznv
The batch stride of an in place tensor is taken from nb[2] as
nb[2] / type_size * block_size, which holds when nb[2] is padded and
not a multiple of nb[1]. A test_mul_mat case with a padded batch stride
covers it.
* vulkan: keep the A and B ranges exact in mul_mm
The quantized A loads of mul_mm carry no row bound and rely on the
descriptor range to read zeros past the last row of a partial tile.
Binding A and B up to the end of the buffer let those tiles read the
leftovers of a previous node and hung the NVFP4 mul_mm on NVIDIA
without coopmat2. The range is the strided extent of a tensor read in
place and the staged size otherwise.
* server : allow splitting RANK pooling for causal LLM rerankers
Rerank models fall into two categories: bidirectional cross-encoders
(BERT, etc.) that require all tokens in a single physical batch, and
causal LLMs repurposed as rerankers (Qwen3, Qwen3-VL) that can use
chunked prefill like any other decoder.
Previously the server rejected all RANK-pooling inputs larger than
n_ubatch, and the graph builder hardcoded QWEN3/QWEN3VL arch checks to
determine last-token pooling. This broke long-document and multimodal
reranking for causal models.
Fix: expose llama_get_causal_attn(ctx) so the server can check the
effective runtime attention type (reflecting any --attention override
or set_causal_attn call). Also expose llama_model_is_causal(model)
for querying the static architectural property from GGUF metadata.
can_split() now permits chunked prefill for RANK pooling when the
context is causal. The graph builder's inline arch check is replaced
with the same cparams.causal_attn predicate, removing the duplication.
Assisted-by: Opencode/Qwen3.8-27B
* remove unused llama_model_is_causal, fix whitespace
Assisted-by: opencode
---------
Co-authored-by: timothywang21 <timothywang21@users.noreply.github.com>
- register --rpc unconditionally and call llama_supports_rpc() only from its handler
- print server "initialization ..." log after args are parsed
Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-RL
Recent PLaMo-3 models use YaRN, while some earlier PLaMo-3 models do not.
The recent PLaMo-3 store their YaRN settings as flat config keys
(rope_scaling_factor, initial_context_length) and build the dict at runtime
in Plamo3Config.rope_parameters. The current converter misses these settings
and writes plain RoPE metadata to GGUF. Mirror the runtime settings into
rope_parameters so the corresponding rope.scaling.* is written to GGUF.
The SYCL FWHT covers 64 to 512 via the standard butterfly network, plus
384/640/768/1280 via the Kronecker/Paley construction added separately in
Hadamard hint can produce (1024, 2048, 4096, 8192); those still fall through
to the default case and run as a dense GEMM against the materialized
rotation tensor, correct but O(n^2) instead of O(n log n).
fwht_kernel_wide runs one row per work-group instead of per sub-group, so
each work-item keeps N/NT values rather than N/WARP_SIZE. Butterflies below
the sub-group width still shuffle; those up to the work-group width go
through work-group local memory; the rest stay in registers. Same butterfly
and sign convention as the existing narrow kernel.
ggml's SYCL backend registration (dpct::dev_mgr) unconditionally requires a
GPU-labeled platform to exist and throws before any op-level test can run,
so test-backend-ops could not be exercised on this box (a GPU-less pod) even
via the CPU device. Verified instead with a standalone harness: the same
kernel body run through a real SYCL CPU device (Intel oneAPI DPC++ 2026.1,
OpenCL CPU backend), checked against an independent recursive-doubling
Hadamard reference, cross-validated by first running the existing unmodified
narrow kernel through the identical harness and confirming it passes (rules
out a reference-convention bug before trusting a pass on the new code).
Random-input results for all four widths, single- and multi-row:
N=1024 NT=256 rows=1 max_abs_err=1.7e-07 max_rel_err=4.9e-04 PASS
N=2048 NT=256 rows=1 max_abs_err=1.9e-07 max_rel_err=2.0e-04 PASS
N=4096 NT=256 rows=1 max_abs_err=2.0e-07 max_rel_err=1.4e-04 PASS
N=8192 NT=256 rows=1 max_abs_err=2.5e-07 max_rel_err=3.8e-03 PASS
N=1024 NT=256 rows=7 max_abs_err=2.4e-07 max_rel_err=1.0e-03 PASS
N=2048 NT=256 rows=5 max_abs_err=3.0e-07 max_rel_err=9.4e-04 PASS
N=4096 NT=256 rows=3 max_abs_err=2.7e-07 max_rel_err=1.7e-03 PASS
N=8192 NT=256 rows=2 max_abs_err=2.5e-07 max_rel_err=1.9e-03 PASS
This covers the kernel algorithm itself; it does not exercise the ggml
dispatch/supports_op integration end to end, which needs a real GPU (or a
SYCL GPU plugin) to get past backend registration. test-backend-ops build
is verified: fwht.cpp recompiles with zero warnings as part of ggml-sycl.
* hex-topk: trying to improve/cleanup the pipeline
* hex-sampling: add STEP op
* hex-sampler: add SUM op
* hex-sampler: update CPY to support sampling cases
* hex-binary: add support for chunking to handle large logits
* hex-argmax: super basic version of ARGMAX
* hex-binary: support for scalars in extended buffers
* hex-binary: fix wrong indexing for dim 1 broadcasts across dim 2 slices
* hex-argsort: fix missing header
* hex-sampler: cleanup dma usage in the sampler related ops, and binary
* hex-build: disable autovectorizer, it is better to use explicit hints for critical loops
* hex-binary: fix perf regression due to is_1d fallback
* hex-ops: update supported ops
* cuda: add F16 input to the FWHT
The CUDA FWHT accepts F32 input only. This makes the source type a template
parameter, so the kernel reads an F16 source directly instead of requiring a
converted copy. The F32 path is unchanged.
supports_op accepts an F16 src1 against an F32 src0 for the Hadamard hint.
Every other F16 src1 against a non-F16 src0 is still refused.
ggml_cuda_op_mul_mat_use_fwht is the single predicate both supports_op and
the dispatch call now share, checking contiguity and same-shape(src1, dst)
in addition to the type/hint conditions above. Without a shared predicate,
supports_op could admit an op that ggml_cuda_op_fwht then rejects only after
the unconditional same-shape assert has already fired; that gap predates
this change (it applies to the existing F32 path too) but this PR is what
touches supports_op, so it closes it here.
test-backend-ops on an A10 (lambdalabs): MUL_MAT 1297/1297, including all
24 Hadamard cases (18 existing F32, 6 new F16).
* cuda: use ggml_cuda_cast in the FWHT load, drop the comment
* llama : add discard for deferred state writes
* llama : add tensor zeroing helper for backends without tensor memset
* llama : clear K/V data after failed sequence restore
* llama : clear recurrent state data after failed sequence restore
* llama : simplify discard and restore cleanup
* llama : report error when abnormal cell count is found in state_read_meta
* llama : clear attention state on hybrid restore failure
* tests : cover failed state restore cleanup
* llama : clear MLA state on dsa restore failure
* tests : update test for rebased test suite
* llama : clarify comment in llama_memory_recurrent::state_read
* Added tiled mul_mat.
For each mul_mat_one_chunk, quants are unpacked into (max) 256x256 tiles of int8,
one routine per quent. Then microkernel computes 16x16 tiles before writing out
256x256 float reults to main memory.
Tests/benches in tests/test-tiled-mulmat.cpp. 3-6x speed improvement
for large matmul, break even at 4096x64 * 64x4096, 80% performance (net
loss) for GEMV. Error rates trivial (order of 1-e04 max, 1-e05 rmse).
* Fixes for ARM/windows builds
* more windows fixes, ggml-cpu.h isn't visible in MSVC for some reason
* unified iqp + tiled on the Q5_K, IQ4_XS set for benchmarking, updated benchmark
* Fixed accidental removal of llama_build_and_test(test-backend-ops.cpp)
* First integration of iqp code
Co-authored-by Bartowski <3266127+bartowski1182@users.noreply.github.com>
* Cleaning up declaration of iq unpacking helpers to align with the bit unpackers
* Removed iqp path
* Fix cross-platform warnings
* Disabling benchmarks unless explicitly enabled
* Fix backend_init for DLL-based builds, add self and bartowski to CODEOWNERS for tiled
* Put benchmarks behind a flag
* kernel fix for AVX2, iq quants
* Fix for asan, leaking memory in test-tiled-mulmat and avoid stack use after return
* guarding env flags with std::call_once
* Simplified repacking for VNNI to a single call per macrotile
* No threadlocals anymore, aligned wdata access
* Doing aligned reads since we ensure alignment with padding in wdata
* Eliminated per-thread gather of Q8_K rows in mul_mat_id, we now gather/repack in a single pass. Repack method now takes pointer array to support both dense/normal and mmid paths. Interface with ggml-cpu.c simplified as a result
* Unified/simplified dispatch and support checks. Put details on wdata needed inside the kernel.h body, simplified interactions with ggml-cpu.c.
* Cleanup includes and whitespace, update src1_repack to return false if we don't need a special repack, so the common case is handled by driver
* Better detection of win32 and additional whitespace fixes
* Gating fuzz tests behind a parameter and some extra prints to try and fix slow CI hosts
* Optimized AVX2 kernel
* Changed interleave format and added ability to interleave in-place after dequant
* Repacks now happen in-place, 16x64 microtiles are independent of each other
* Only repack rows in groups of 16 as they're needed. Save work in low n_rows cases and optimize L1 usage in other cases
* Use long panels for memory-bound regime (M <= 16), reintroduce IQP path for benchmarks
* Fix unused warnings and cleanup. Improved IQ dequantization speed.
* Removed separate process benchmarks
* Revert "Removed separate process benchmarks"
This reverts commit 0688cf43d5.
* AVX2 optimizations and guards for tests on windows
* Removed temp perf harness
* Remove perf-mulmat from build
* Removed IQP path, simplified tests to not use sub processes
* Cleaning up alignment of wdata
* Whitespace fixes and aligning L2 workspace to clean 512kb boundaries
* Update ggml/src/ggml-cpu/tiled/tiled-kernel.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Cleanup merge-duplicated declaration of test-backend-ops target
* Undo accidental line deletion in ggml.c
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
The original function was broken on Windows for some unicode paths
Paths without a trailing separator now create the last directory too,
matching the function name. All current callers already include a
trailing separator, so this change does not affect them.
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
which resulted in different greedy transcripts for 4.5% of English and 6.5% of Japanese
test utterances. In Japanese, some differences changed entire words.
This change:
* uses `log(x + 2^-24)` instead of clamping to the log floor
* uses a symmetric Hann window, equivalent to `torch.hann_window(periodic=False)`
* adds the normalization epsilon to the standard deviation instead of inside the square root
Only the `lfm2a` preprocessor opts into these behaviors. Other audio preprocessors are unchanged.
Tested on top of 84e76d8 using `llama-server` with CUDA and `temperature=0`, compared against
http://github.com/Liquid4All/liquid-audio fp32.
Test set:
* 200 LibriSpeech `test-clean` utterances (EN)
* 200 Common Voice `ja` test utterances (JP)
* identical 16 kHz audio passed to both implementations
| Greedy transcript identical to `liquid-audio` | Without fix | With fix |
| --------------------------------------------- | ----------: | ----------: |
| EN F16 | 191/200 | 200/200 |
| JP F32 | 187/200 | 200/200 |
| JP F16 | 187/200 | 199/200 |
The remaining JP F16 difference is a comma and matches the reference implementation's own bf16
output.
Mel relative L2 error versus `liquid-audio`:
* EN: 3.2% -> ~2e-6 median
* JP: 3.9% -> ~2e-6 median
* opencl: add A8 Q5_K non-MoE non dp4a + dp4a binary kernel
* opencl: fix s transpose - s only transposed for bin kernels
---------
Co-authored-by: Li He <lih@qti.qualcomm.com>
Aleksander Grygier