* mtmd : add Nemotron 3 Nano Omni support (parakeet)
This commit adds support for the subsampling and encoder part of
Nemotron Nemo 3 omni model.
The Parakeet subsampling/encoder were taken from parakeet.cpp which
is currently a pull request against whisper.cpp. I've tried to copy the
code a close as possible to hopefully enable easy patching between the
these two project later.
Refs: https://github.com/ggml-org/whisper.cpp/pull/3735
* mtmd : generate rel pos tensor in graph instead of in conversion [no ci]
This commit removes the generation of the relative positional tensor in
the model conversion script and instead computes it in the encoder
graph. This is only done for the window of positions required for the
current audio sample.
* mtmd : add clip_get_model to clip API [no ci]
This commit adds a function to get access to the clip_model. It also
removes the two functions clip_get_mel_filter_tensor, and
clip_get_window_tensor(const struct clip_ctx * ctx) which can now use
clip_get_model to access the model tensors that it needs.
* mtmd : read mel_filters and window into hparams
* mtmd : use set_input_f32 lambda [no ci]
* mtmd : add better asserts for mel_filters and hann window [no ci]
* mtmd : add missing size_t cast
* mtmd : change type of pad to size_t
* mtmd : zero initialize samples_padded
* mtmd : remove unsued ctx member from parakeet preprocessor
* mtmd : make log_mel_spectrogram_parakeet_worker_thread private static
* mtmd : sync/update parakeeet impl with latest whisper.cpp
This commit updates the parakeet code in mtmd to reflect the latest
updates to parakeet.cpp in whisper.cpp.
A follow up commit will address the currently hardcoded dw_pad and see
if we can add n_conv_kernel as a model metadata field.
* mtmd : add audio_conv_kernel_size to model conversion
This commit updates the model conversion to read the conv_kernel_size
field from the sound_config section of the models config.json file.
It then uses this field instead of the hardcoded values in parakeet.cpp.
* mtmd : cleanup [no ci]
* conversion : call super().filter_tensors [no ci]
* do not discard result of super filter_tensors
* mtmd : use build_mm instead of ggml_mul_mat
* mtmd : use build_ffn
* mtmd : move and reuse get_vector lambda
* mtmd : use build_inp_raw for parakeet
* mtmd : throw exception in get_scalar instead of assert
* mtmd : fix std::min call
* mtmt : use .c_str in throw clause in get_vector
* mtmd : check for F32 type and non-empty tensor in get_vector
The get_vector lambda is used by get_scalar but also standalone to read
in the mel_filters and the window data. Therefor we are not checking
for 1D tensors but allowing multiple dimensions. We do have a check in
get_scalar to verify the size of the vector.
* mtmd : replace hardcoded 1101 for n_tokens_real
* mtmd : assert subsampling_factor is 8
This commit adds an assert of the parakeet subsampling factor to check
that it is 8.
The motivation for this is that this model currently has three
convolutions with a stride of 2. If the underlying model updates the
subsampling factor these convolution operations will need to be updated
and this will produce and error if this occurs.
* mtmd : remove unused ggml_tensors attn_pos_w and mm_norm_w
* mtmd : remove single thread path
This commit removes the single thread path which was a left over from
the original parakeet.cpp where n_threads is configurable.
* fix some security issues
---------
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* spec: add DSpark speculative decoding
DSpark (DeepSpec, 2026) on top of the merged DFlash drafter. It reuses the
DFlash encoder/decoder graph, target feature extraction and KV-cache injection,
and the verify/accept path unchanged; the draft model is a new "dspark" arch
adding a low-rank Markov head (markov_w1/w2) and an optional (unused here)
confidence head. No new public APIs.
The proposal is the only change: the block is anchor-first (position 0 already
predicts the first draft) and the decoder graph applies a semi-autoregressive,
previous-token conditioned logit bias in-graph, chained per block position:
logits'(i) = logits(i) + markov_w2 . markov_w1[prev(i)]
prev(0) = the block's anchor token, prev(i>0) = argmax(logits'(i-1))
vectorized across all blocks in the batch; the anchors are fed through a
dedicated graph input (token 0 of every block). Greedy stays lossless
(verify unchanged, same as DFlash).
- new arch "dspark" (llama_model_dspark : llama_model_dflash, reuses the graph,
loads the markov/confidence tensors; shares the target's embed/lm_head).
- Qwen3DSparkModel converter.
- new spec type "draft-dspark" (common_speculative_impl_draft_dspark :
common_speculative_impl_draft_dflash, overrides draft() only: submits whole
anchor-first blocks and greedily reads back the biased logits).
* spec: read draft block size in the dflash impl
* docs: add DSpark section to speculative.md
* spec: keep dspark block size read in the dspark impl
* dspark : add TODOs for incomplete parts
- confidence head is loaded but not used yet
- confidence-scheduled prefix pruning is not implemented
- the in-graph Markov chain is greedy-only
- only Qwen3 backbones are supported for now (also noted in docs)
* spec: fold DSpark into the DFlash arch
Address review: drop LLM_ARCH_DSPARK and the dspark.block_size /
markov_rank GGUF keys. A DSpark draft now converts to a DFlash GGUF;
the Markov head tensors are detected by presence (like eagle3 d2t),
block_size is read from the existing dflash.block_size key, and the
block anchors are taken as a strided view of the decoder's token
input instead of a separate graph input.
* spec: add confidence-based draft pruning for DSpark
The DSpark confidence head predicts per-position acceptance of the
drafted block. --spec-draft-conf-min truncates the block at the first
position below the threshold (default 0 = disabled).
* fold the dspark impl into dflash, selected by spec type
* address review comments
* dspark: clean up and improve naming
* update readme
* remove trailing whitespace
* dflash: draft full n_max blocks, defer dp.n_max to the central truncation
The DSpark markov head views the draft batch as a uniform [n_seqs x block]
grid, but the per-seq dp.n_max clamp could produce blocks of different
sizes, silently corrupting the strided views and the resulting logits.
Drop the clamp and always draft the full n_max block for every sequence:
dp.n_max is already enforced by the central truncation in
common_speculative_draft(), the same way eagle3 handles it.
Co-authored-by: Zaire404 <3147879462@qq.com>
* dflash: assert the markov head block-uniformity invariant, require the conf head
With the draft batch always submitting equal-size n_max blocks, a
non-divisible token count can only mean the batch was split across
ubatches or a caller broke the layout - fail loudly instead of silently
dropping the markov bias. The block_drafts > block_size early return
stays: worst-case graph reserve passes legitimately build with
n_seq_tokens > block_size.
Also make conf_proj required when the markov head is present: the
confidence head is part of the DSpark checkpoint format, and a missing
head would otherwise leave --spec-draft-conf-min silently reading stale
embeddings instead of confidences.
Co-authored-by: Zaire404 <3147879462@qq.com>
* dspark: fold conf_min into p_min
p_min and conf_min express the same thing - the minimum predicted
survival probability for a drafted position - differing only in how the
estimate is obtained: token probability for regular drafters, the
trained confidence head for DSpark. The DSpark readback never used
p_min, so reuse it for the confidence threshold and drop the separate
--spec-draft-conf-min flag. Both defaulted to 0 (disabled), so behavior
is unchanged.
Co-authored-by: Zaire404 <3147879462@qq.com>
* dflash: note the confidence broadcast workaround
Requested in review: the ggml_repeat only adapts the [1, n_tok]
confidences to the n_embd-wide embd_nextn transport so that
llama_get_embeddings_nextn can be reused - not a placeholder.
Co-authored-by: Zaire404 <3147879462@qq.com>
* cont : clarify
[no ci]
---------
Co-authored-by: Ruixiang Wang <wangruixiang07@outlook.com>
Co-authored-by: Zaire404 <3147879462@qq.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* server + ui: refactor resumable stream routes to query string conv_id
The conversation id can embed a model name containing slashes
(ggml-org/...) in router mode, which the decoded path splits before the
:conv_id param is captured, so stop and resume never matched the
session. Move the id to the conv_id query string on the public routes
and on the internal router -> child hop, where slashes survive
encoding. Handlers are unchanged since query and path params land in
the same map. Add a regression test with a slashed model name.
* server: move stream route docs to server-stream.h
Address review: ngxson wants the main server.cpp registration code kept
clean and simple, with route-level explanations living in the header.
Move the query string rationale and the lookup ownership note next to
the handler declarations in server-stream.h, and shorten the wiring
comment to a pointer.
* server: cancel a pending request when its stream is stopped during model load
The conversation was registered in the conv map only after the blocking
autoload wait, so a stop issued while the model loaded found nothing to
cancel and the request went on to generate an orphan once the load
ended. Register the conversation before the wait and give the entry a
ticket: a stop erases the entry, and the parked request checks its
ticket after the wait and aborts with 400 instead of starting. A newer
request on the same conversation replaces the entry, so only the
stopped request is cancelled. Add a regression test that stops during
the load window.
* server + ui: resume a stream after a page reload during model load
A pending request died with the client socket when the page was
reloaded while its model was loading, so no session ever existed and
the conversation had nothing to recover. A session request that waited
for a load now detaches from the client socket and reaches the child
regardless, the session buffer receives the generation, and the resume
route answers 503 while the owner is loading so the client retries
instead of dropping its state. The WebUI persists the pending stream at
send time, quietly polls on 503, and attaches once the session exists.
Add a regression test that drops the client during the load window.
* ui: show the model load progress again after a page refresh
The resume wait was invisible, so a conversation refreshed while its
model was loading showed nothing until the first byte. On a 503 from
the resume probe, mark the conversation as loading again so the
assistant row persisted at send time renders the processing info, and
target the model frozen in the persisted stream state for the
progress, since the row has no model yet and the dropdown may not be
restored.
* fix CI
* fix CI bis
* Add preliminary MiniMax-M3 support
Text-only port that re-uses existing components: MiniMax-M2 style GQA with
per-head QK-norm and partial rotary, DeepSeek-V3 style leading-dense and
routed/shared experts, and swigluoai activation. Sparse attention is not
yet supported (dense fallback); vision tower and MTP heads are dropped.
* MiniMax-M3 vision tower (mmproj + clip graph)
* Delete m3_vision_ref.py
* Update clip.cpp
* MSA
* Update constants.py
* Update minimax.py
* Cache creation. Working withotu flash attention
* Added flash attention for sparse layers
* Decomposed slow cpu OP into GPU + CPU ops. Massive speedup over long ctx
* Rewrote indexer op to be cuda native. Modified flash attention to match per group block picking
* Implement sparse attention calc out of stock ops.
* Fix a cache allocation and cont issue
* Fixed -fa auto crash, flagged debug spots
* Delete vocab.json
* Delete model.safetensors.index.json
* Delete generation_config.json
* Delete Minimax directory
* Handled multi stream case to fall back on Dense Attention
* Development scaffolding cleanup. No functional change to the decode or
4-way paths. Full debug harness remains at <8136a9c68ed7a5eb009aa67bba3fda8062f4648f> for reproducing the
selection-parity validation.
* Remove redundant comment from minimax-m3.cpp
* Changed 3 Gelu Ops for vision into Gelu_erf ops
* Assert that n_kv is multiple of 128
* Rename MSA index tensors to indexer convention
Note: All GGUFs generated before this change will need to be regenerated.
* Fix incorrect Assert
* Review driven changes (#3)
* Remove comment from conversion minimax.py
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* Remove whitespaces from constants.py
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* Tighten comment in minimax.py
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* inherit MiniMax-M3 from MiniMax-M2
* drop dead text_config fallbacks
* Add indexer writer methods
* Reuse LLM_FFN_SWIGLU_OAI_MOE
* Remove duplicate indexer setters, add only block_size/local_blocks, follow value naming convention
* Fix conversion error /gguf_writer.py
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* Update gguf-py/gguf/tensor_mapping.py
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* Update conversion/minimax.py
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* Update conversion/minimax.py
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* Remove whitespace in src/llama-kv-cache.cpp
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* Remove Whitespace in Update src/llama-model.h
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* Remove whitespace in src/llama-hparams.h
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* Update minimax_m3.cpp
Rewrite code comment based on feedback and to better reflect the actual architecture, and reuse existing build_vit
* Rename minimax_m3.cpp to minimax-m3.cpp
* Update CMakeLists.txt
* Remove debug code from clip.cpp
* Update clip.cpp
* Update comments in tools/mtmd/models/minimax-m3.cpp
* Permute Q/K at conversion, drop precomputed sin/cos
* Log cache size on launch, block ctx shift, support prompt caching
Log indexer cache size on launch
Disallow ctx shift
Support prompt caching
* Update minimax-m3.cpp
* Optimize implementation, add multi stream support.
Fully rewrote minimax-m3.cpp for speed and buffer size gains:
Unified the 4-way + decode, 1 FA call per layer instead of 4, with the groups mapped onto ne[3]
Custom CPU op now emits block-level mask, expanded on GPU, which causes CPU to GPU transfer to shrinks at prefill
Decode: ~25 nodes/layer vs ~50, no per-group concats/conts
Unified selection semantics, so both regimes rank bs + local bias (position-anchored local force), which means prefill/decode can no longer disagree on selection
can_reuse on the MSA bias input. Graph reuse at decode restored (was rebuilding the full graph every token)
In-place mask adds, shrinking compute buffer ~6.8 to ~4.2 GiB at ub2048/62k
Multi-stream: MSA now runs with -np N when kv_unified=false. Decode stays batched across streams (still 1 FA call), prefill loops per stream. dense fallback only for --kv-unified + multi-seq
Measured effect on expert offload bound setup: decode 6.2(4WAY)–7.15(MSA_decode) -> 7.7~7.8 t/s, flat from 5k to 60k+. prefill around 10% faster. buffer about 20% smaller, multi-user support.
* set default cache type to F32
* Fix potential DSA double indexer cache allocation bug, only allocate in-cache k_idx for archs that opt in
* remove F16 downcasts in MSA attention, force F32 indexer score accum
* Add Minimax eos to llama vocab
* Guard edge case where idx cache can become stale after a tail trim
* Update llama-kv-cache.h
* Update llama-kv-cache.cpp
* Update llama-kv-cache.cpp
* Update llama-kv-cache.h
* Change resize Pad to none, resize alg to Bicubic Pillow
* Review driven changes
* Update llama-kv-cache.cpp
* rm unrotated pos_t
* fused rope w + pad
* rename merge --> merger for consistency
* add review skill for mtmd
* graph should use hparams n_merge
* fix lint
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
The in-conversation system-message edit textarea had a fixed min-height and no auto-resize, so long messages were crammed to 2 lines. Reused existing autoResizeTextarea helper on input and when the editor opens, and added max-height to cap growth.
* ui: detect the conversation import format from file contents
iOS resolves every accept entry to a UTI and has none for ".jsonl", so the
picker greyed out exported conversations. Drop the accept filter and pick the
parser from the file contents: ZIP magic bytes, then a first "session" record
for JSONL, otherwise the legacy JSON format.
Also remove the unused importConversations() picker and an orphan doc comment,
and cover each format with unit tests.
* ui: report what a conversation import actually wrote
The import summary echoed the selection back, so re-importing conversations
already in the database claimed success while nothing was written and only a
console warning said otherwise.
Return the imported and skipped conversations from the database layer, list the
written ones in the summary, and count the rest in a toast.
* ui: name the literals of the JSONL conversation format
Introduce SessionRecordType and SESSION_HARNESS, and reuse the existing
NEWLINE constant, so the record format lives in one place.
This also covers the writer side, which predates the import path under
review and carried the same literals: an enum stated by the reader alone
lets the two sides drift.
Values are unchanged, so an export stays byte identical.
* add common/subproc.h|cpp
* add compile flag LLAMA_SUBPROCESS
* disabled by default on android and ios
* test-jinja: use common subproc
* mtmd: disable video if subproc is not set
* disable subproc on wasm
* make is_created atomic
* migrate server-mcp
The context gauge card starts monitoring like the dial does, because
its own processing state instance only follows the live stream while
its monitoring flag is set. It also gets back the text-sm and ring
classes the removed hover card wrapper used to inject, which restores
its layout. Routing to a conversation now lands at the bottom
instantly and keeps the pin one frame at a time until the page height
settles, since content-visibility size realizations and syntax
highlight passes grow the page without DOM mutations.
* move server_pipe to common
* init impl
* vendor: update subprocess.h
* add server_mcp_stdio
* stderr drain
* server_mcp_transport
* server_mcp_stdio is now framing-only, no json
* internal/mcp-stdio: integration + tests + fixes (#26075)
* server-mcp: harden transport and wire up the tool integration
Builds on the transport/manager architecture (server_mcp_transport + server_pipe)
with the hardening and integration the draft did not yet have.
Hardening:
* Reader and stderr pumps are polled (running-aware) instead of blocking on a read
that only ends at EOF. subprocess_terminate() SIGKILLs only the direct child, so a
grandchild the MCP server spawned that inherited the pipe would otherwise keep the
write end open and hang teardown (both warmup shutdown at startup and process
shutdown). The writer is likewise non-blocking + polled.
* Windows: resolve the command through PATHEXT so "npx" (npm ships npx.cmd, never
npx.exe) spawns, matching POSIX's PATH search; and enumerate the parent environment
as UTF-8 (GetEnvironmentStringsW) instead of the active code page.
* server_pipe gains an opt-in max_size (default unbounded, so the router's streaming
use is unchanged); the MCP reply queue uses it so a server that streams unsolicited
notifications between requests cannot grow it without bound.
Integration:
* --mcp-servers-config / --mcp-servers-json flags; enabling MCP restricts default CORS
to localhost, same as --tools.
* MCP tools are exposed through /tools (and chat-completions) as <server>_<tool>,
skipping names that collide with a built-in or another MCP tool.
* Manager lifecycle wired into llama_server(): warmup at start, shutdown() from the
signal handler before the HTTP server drains, blocking teardown in clean_up().
* SIGPIPE ignored so a child dying mid-write yields EPIPE rather than killing us.
Assisted-By: Claude Opus 4.8 <noreply@anthropic.com>
* server-mcp: add MCP test suite with grandchild deadlock regression test
21 tests over the /tools endpoint: tool discovery/invocation, timeouts, crash
recovery and respawn cooldown, warmup partial failure, malformed and batched
notification+response output, tool-definition shape, and prompt shutdown during a
slow call.
The last test spawns an MCP server that leaves a grandchild inheriting its
stdout/stderr and asserts the server both starts and stops promptly. Verified it
fails (5s SIGKILL fallback on a deadlocked reader-join) when the pump is made to
ignore the running flag, and passes with the polled reader.
Assisted-By: Claude Opus 4.8 <noreply@anthropic.com>
* clean up
* clean up 2
* even stricter life cycle
* nits
* nits 2
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* fix some edge cases
* fix last_error data race
* fix response schema + docs
* server: fix MCP zombie leak and timeout-induced transport teardown
join_pumps() never reaped the child, leaking one zombie per spawn:
call subprocess_join() before subprocess_destroy().
A per-call timeout permanently closed from_server and got a healthy
transport evicted: add close_on_stop to server_pipe::read() and pass
false from send_rpc(), where should_stop is a per-request deadline
and a late reply is already skipped on id mismatch.
Also drop the unreachable disconnect cancellation in
server_mcp_tool::invoke(): support_stream is false, st is always null.
(cherry picked from commit e6de1ec043174fd0570b1e60d47f06c7c19d620d)
Assisted-by: Claude Opus 4.8
* server: make MCP test fixtures JSON-RPC 2.0 compliant
Add the missing notification guard to mcp_malformed_server.py and
mcp_burst_server.py (the latter treated id 0 as a notification and
replied to unknown ones; its notification table is now unused).
Return -32602 instead of -32601 for unknown tools: tools/call is a
valid method, the tool name is the invalid parameter.
Also fix the test module docstring: tools are named <server>_<tool>.
(cherry picked from commit 74a08e8c311dabf3b49d06cc6d754b0097ae7a38)
Assisted-by: Claude Opus 4.8
---------
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
Co-authored-by: Pascal <admin@serveurperso.com>
These two parameters were overlooked when task parameters were being JSONized
within `generation_settings` and have been added. A regression test has been
added to prevent the problem from recurring and it passes.
Fixes#25803
* common : extract trie/ac to a separate file
* common : support multiple token sequences in the reasoning budget sampler
* common/trie : return matched word index
* common/trie : rename "word" to "pattern"
* common/reasoning-budget : expose matched end sequence
* common/sampling : replay end sequence when reasoning budget is done
* cont : update to use multiple end sequences
* cont : clean up
* performance harness - the empirical root
Assisted-by: Claude Opus 4.8
* 210.36ms -> 2.67ms per streamed token
Assisted-by: Claude Opus 4.8
* 11.58ms -> 0.62ms per streamed token
Assisted-by: Claude Opus 4.8
* 22.02ms -> 3.33ms per streamed token
Assisted-by: Claude Opus 4.8
* 3.07ms -> 1.36ms per streamed token at 40 messages
Assisted-by: Claude Opus 4.8
---------
Co-authored-by: Zach Winter <dmtommy@icloud.com>
* ui: remove viewport fade in and smooth autoscroll bottom snap
fadeInView mounted every message and markdown block at opacity 0 and
relied on an IntersectionObserver to reveal it. When the observer never
fires (blocks mounted offscreen during long agentic loops) the content
stays invisible forever while still present in the DOM. Remove the
action, its orphaned isElementInViewport util and all call sites:
blocks now render visible immediately.
AutoScrollController.scrollToBottom defaulted to behavior smooth and is
invoked every 100 ms while streaming. Each tick restarts an easing
animation toward a moving scrollHeight, producing a random elastic bump
of a few pixels when the user reaches the bottom and autoscroll
reengages. Default to instant scrolling; the user facing scroll down
button keeps its smooth behavior.
* ui: skip rendering of offscreen chat messages via content-visibility
Apply content-visibility auto with contain-intrinsic-size to chat
messages so the browser skips layout and paint for messages outside
the viewport. The DOM stays complete: component state, find-in-page,
text selection, and the mutation based autoscroll are unaffected, and
browsers without support simply ignore the properties.
* ui: remove conversation switch fade
Switching conversations faded the message list out and in over 500 ms
plus a 300 ms route delay, deferring the message refresh behind two
requestAnimationFrame calls. Remove the fade, its navigation hooks and
dead state, and refresh messages directly so switching is only bound
by actual render time.
* ui: describe present behavior in comments and drop unused parameter
* ui: anchor the context gauge popup to the form with plain CSS
The stats card was portaled to body and repositioned in script on
every ancestor scroll event, trailing the page by one frame while
streaming. Render it as an absolutely positioned sibling of the input
box inside the already relative form, so nothing runs during scroll.
The card sits just above the dial, centered on it and overlapping the
textarea, from a single measurement of the dial center and top taken
when it opens; the dial and the card share the same positioning frame,
so the values stay exact while the card is open. Mouse pointers open
on hover with a grace delay to reach the card, touch pointers toggle
on tap, any press outside the card and the dial closes it, and Enter
and Space toggle from the keyboard. The card lives outside the input
box because its overflow-hidden and backdrop-filter would clip any
positioned descendant.
* ui: extract context gauge popup constants and relocate its state store
Move the placement values and the close grace delay to
lib/constants/context-gauge-popup.ts, matching the auto-scroll
constants layout, and move the popup state module from the component
folder to lib/stores where runes modules live in this codebase.
* ui: declare the context gauge popup card ref as $state
* ui: fix MCP server display name conflicts in tools lists
Tool groups were keyed by display label so two servers reporting
the same name broke the keyed each blocks and only one was visible.
Key rendering, expand state and toggles by the stable server id
instead, and suffix duplicate labels with a counter in config order.
* ui: customizable MCP server display name with autofill
Add a display name field to the MCP server form, add and edit alike.
The custom name takes precedence over the server-reported one, so two
servers reporting the same name can be told apart; clearing the field
returns to the automatic label. In the add dialog a debounced preview
handshake prefills the field with the server-reported name: a manual
edit freezes the autofill, stale responses are discarded, failures
stay silent, and an unedited prefill is not persisted so the label
keeps following the server.
* ui: fix recursive fetch passthrough in the client test setup
The original fetch was captured inside beforeEach, where it is the
previous test's spy since vi.spyOn returns the existing one, so the
default passthrough recursed on itself for any URL outside the
mocked set. Capture the real fetch once at module load.
* support "reasoning_effort": "none" in OAI API
* handle reasoning.effort: "none" in OAI responses API
* clarify non-"none" values of reasoning_effort have no effect
* use json_value instead of body.at
* ui: add Default reasoning option that defers to the server
The webui always injected enable_thinking, overriding the chat template
default and the --reasoning flag, breaking models that reason
unconditionally (e.g. Gemma 4 E4B) on a fresh client.
Default sends nothing so the server decides, Off and effort levels
force the value as before. All choices are remembered.
Also remove the boolean thinking API from the conversations store and
drop ChatFormReasoningEffortSubmenu.svelte (dead code).
* ui: close the whole menu tree on reasoning level selection
The reasoning levels were raw buttons inside the SubContent, so
selecting one only closed the submenu via manual state while the root
dropdown stayed open. DropdownMenu.Item closes the full tree on select
like the sibling entries and brings native keyboard navigation.
* ui: prevent the add menu tooltip from flashing when the dropdown closes
* feat(ui): add symbolic math support to JS sandbox via nerdamer
Preload nerdamer (with decimal.js) in the sandboxed worker,
exposing the `nerdamer` global for symbolic computation:
simplify, expand, factor, diff, integrate, solve, laplace,
ilt, limit, partfrac, gcd/lcm, roots, coefficients, and more.
Mirrors the math.js integration pattern from the
feature/sandbox-symbolic-math branch, but uses nerdamer
for a lighter, more focused symbolic math engine.
* Update sandbox-harness.ts
* docs(ui): update sandbox tool description with detailed nerdamer usage guide
* Clarify nerdamer usage in sandbox tool description
Updated the description of the sandbox tool to clarify usage of nerdamer.
* ui: build nerdamer sandbox prelude from vendored source
Replace the vendored all.min.js with the readable nerdamer-prime
source and its two bundled deps (big-integer, decimal.js), licenses
included. A vite plugin bundles and minifies them at build time with
the upstream esbuild flags, exposed as virtual:nerdamer and imported
lazily on first sandbox use. The vendors package.json pins commonjs
so the project level type: module does not break esbuild format
detection. The harness gains a CSP removing network egress from the
worker, and browser tests cover the prelude, exact arithmetic, the
fetch block and the timeout.
Upstream snapshot: together-science/nerdamer-prime@1936145
* feat(ui): make symbolic math (nerdamer) a user-toggleable setting
- Add SYMBOLIC_MATH_ENABLED setting key and registry entry (checkbox, default false)
- Convert SANDBOX_TOOL_DEFINITION to buildSandboxToolDefinition(includeSymbolicMath)
so the tool description includes/excludes nerdamer API docs dynamically
- Cache sandbox harness per variant ('nerdamer' / 'plain') for instant toggle
- Deprecate SANDBOX_TOOL_DEFINITION constant alias for backward compatibility
- Update tools store to pass symbolic math config into tool definition
* docs(ui): tell LLM to list nerdamer functions first, do not guess
* test(ui): enable symbolic math in sandbox tests via settingsStore config
* style(ui): fix formatting for tools.svelte.ts
---------
Co-authored-by: Pascal <admin@serveurperso.com>
The Qwen3-VL learned position embedding is interpolated to the runtime patch
grid with the default bilinear+antialias (align_corners=False) sampling, while
the transformers reference uses align_corners=True (torch.linspace(0, side-1, T)).
The mismatch scales grounding coordinates about the image center, growing with
image size and per-axis for non-square images (see #16880).
* server: return 400 instead of 500 on validation error with X-Conversation-Id
set_req() attaches the spipe as soon as the header is present, before the request
body is parsed. When params validation throws, set_next() never runs and next_orig
stays empty, so on_complete() called it and crashed with std::bad_function_call,
turning the prepared 400 JSON into a generic 500.
on_complete() now treats an empty next_orig as "streaming never started" and evicts
the session installed by set_req(), so a failed request leaves nothing behind for
discovery or replay. This also covers valid requests that carry the header but do
not stream, which previously left an empty finalized session in the map until the
GC TTL.
* ui: do not send the backend_sampling placeholder
On a fresh profile the syncable settings hold the empty string placeholder meaning
"let the server decide". Every neighbor field goes through the hasValue() guard
that filters it, except backend_sampling, which sent the placeholder verbatim and
made every default settings completion fail validation.
Guard the field with hasValue() like its neighbors. hasValue(false) is true, so an
explicit false still reaches the server and the intent of #18781 (send both true
and false) is preserved. Only the placeholder is filtered.
* feat: WIP
* feat: Replace conversation rename flow with unified AlertDialog component
* feat: Add radio group component and consolidate title generation settings
* refactor: Remove JS Sandbox global toggle and migrate legacy user state
* chore: Formatting
* refactor: Cleanup
Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>
* refactor: Cleanup
* refactor: Marquee selection hook
* feat: UI improvements
* refactor: Bulk db operations
* fix: optimize bulk conversation deletion to handle ancestor chains
* refactor: remove pairedKey mechanism from settings system
* fix: remove redundant onclick handler from dialog cancel button
* chore: pin @lucide/svelte to exact version
* feat: Run JavaScript tool disabled by default
* fix: correct active conversation deletion tracking in bulk delete
* feat: improve shift-key multi-selection support in sidebar via keyboard
* refactor: Retrieve JS Tool enabling via Developer Settings
* nits: sync, dialog wording, cycle guard, and lockfile follow-ups
- Restore titleGenerationUseLLM registry entry so it syncs across devices again
- Mention fork cascade in the bulk delete confirmation dialog
- Clear newParent on cycle guard break so children never point at a deleted conversation
- Align @lucide/svelte in package-lock.json with the exact pin in package.json
---------
Co-authored-by: Pascal <admin@serveurperso.com>
Edge paragraph margins are now zeroed at the source in
markdown-content.css, but the user bubble and the system message still
carried the -my-4 compensation for them. The uncompensated negative
margins shrank the wrapper 2rem below its content, collapsing
single-line user bubbles into a scrollable sliver and skewing the
system message expand threshold.
* ui: fix Show tool call in progress toggle ignored
The showToolCallInProgress setting was disconnected from the render
path during the agentic content rework: getDefaultExpanded() returns
a hardcoded false for tool call sections and an unconditional effect
auto-expands the currently executing tool call regardless of the
setting.
Drive default expansion of all tool call section types from the
setting and remove the now redundant auto-expand effect. Manual
toggling still takes precedence over the default in both directions.
* ui: rename Show tool call in progress to Always show tool call content
The previous name suggested symmetry with Show thought in progress,
which only applies while inference is running, but tool call content
stays expanded after completion. Rename the label, the settings key
and the syncable server key to alwaysShowToolCallContent. The synced
parameter never worked under its previous name so no migration is
needed.
The agentic gate counted MCP servers, builtin and custom tools but not
frontend tools, so with the JS sandbox as the only tool source the
agentic flow was skipped, no tools field reached the server and the
chat template rendered without the tool system prompt.
The sandbox is fully client-side: frontendTools derives from the
Developer settings toggle alone, counting it in the gate restores that
single source of truth.
* common: auto-download dflash- and eagle3- HF sidecars
Mirror the existing mtp- sidecar logic to support auto-discovery and
download of DFlash (dflash-) and Eagle3 (eagle3-) speculative decoding
sidecars from Hugging Face repos.
Changes:
- Add --dflash and --eagle3 CLI flags to trigger sidecar download
- Add find_best_dflash() and find_best_eagle3() using find_best_sibling
- Exclude dflash- and eagle3- filenames from primary model selection
- Filter dflash- and eagle3- from cached model listings
- Wire download tasks that set speculative.draft.mparams as fallback
Assisted-by: pi:llama.cpp/Qwen3.6-27B
* docs : regen
* feat: Add shimmer text animation for processing state indicators
* feat: Redesign CollapsibleContentBlock component with improved UX
* feat: Add conditional setting display support with dependsOn field
* feat: Add showAgenticTurnStats setting for per-turn statistics
* feat: Update ChatMessageAgenticContent with improved UI and new features
* feat: Enhance file read tool UI/UX
* feat: Refine styling of collapsible content and code preview blocks
* feat: add terminal variant to CollapsibleContentBlock
* feat: add built-in tools UI registry
* feat: extract ChatMessageReasoningBlock and ChatMessageToolCallBlock
* refactor: simplify ChatMessageAgenticContent to use extracted blocks
* fix: correct markdown content block margin spacing
* fix: reorganize SettingsChatFields layout and reset button positioning
* fix: use direct map access in agentic store session methods
* refactor: remove reasoning preview/throttle system from CollapsibleContentBlock
* feat: add auto-scroll to reasoning block and remove showThoughtInProgress
* feat: add ChatMessageToolCallDateTime component and support for new tool types
* feat: improve auto-scroll reliability in reasoning block with RAF coalescing and MutationObserver
* feat: show MCP server favicon for tools without a built-in icon
* feat: add search-results parsing utilities and tests
* feat: add ChatMessageToolCallSearchResults component
* feat: integrate search results rendering into ChatMessageAgenticContent
* feat: display tool call input alongside output in ChatMessageToolCallBlock
* style: use muted foreground color in reasoning block content
* chore: Format
* feat: Refine reasoning block layout and make pending thoughts display configurable
* feat: Stream tool call code blocks with auto-scroll and handle partial JSON
* feat: add streaming permission gate infrastructure
* feat: wire permission gate into the agentic loop
* fix: bail out on abort and skip already-approved tool calls
* fix: clear partial tool calls on abort and savePartialResponse
* test: cover partial tool call cleanup end-to-end
* refactor: Remove streaming permission gate logic
* fix: Correct autoscroll and streaming gates for tool calls and reasoning blocks
* refactor: Chat Message Assistant componentization
* fix: Show health metadata for disabled MCP servers and promote connections on enable
* fix: Inherit global enabled state for missing MCP per-chat overrides
* refactor: Cleanup
* refactor: Split ChatMessageToolCallBlock into dedicated components
* feat: Add live streaming and auto-scroll for tool execution output
* feat: Add line numbers and change markers to file edit diffs
* chore: Formatting
* feat: Add type definitions and utilities for recommended MCP servers
* feat: Add recommended MCP servers configuration and storage key
* feat: Add McpServerCardCompact component for recommended servers
* feat: Add recommended servers section to Add New Server dialog
* feat: Update McpServerForm to support authorization requirements
* feat: Add select-none classes for text selection prevention
* feat: Add recommended MCP server icon assets
* refactor: Store dismissed MCP recommendations as a boolean flag
* feat: Render tool results as JSON or Markdown based on detected content type
* feat: UI improvement
* feat: Render search block early and update heading to show execution state
* fix: Prevent non-web-search tools from triggering the search UI block
* refactor: Cleanup
* refactor: Extract hardcoded icon size classes into shared constants
* refactor: Extract hardcoded tool result separator into a shared constant
* refactor: Tool Calls UI/logic
* refactor: Cleanup
* refactor: Cleanup
* refactor: Cleanup
read_file with append_loc emits "{n}\u2192 {line}". The space after the
arrow is meant as a separator, but it is indistinguishable from real
indentation. Models strip "{n}\u2192" yet keep the space, so the old_text
passed to edit_file carries a phantom leading space and never matches
(normalize_for_fuzzy_match trims trailing whitespace only, never leading).
Drop the separator space so the arrow abuts content: stripping "{n}\u2192"
now yields the exact line with its real indentation preserved, and the
failure mode cannot occur by construction. Update the description example
to match the new format.
In MODEL mode, modelPropsCache is never populated: fetchModelProps
call sites are gated on router-only state (isRouterMode checks,
routerModels always empty), so supportsThinking always reads an
empty chat template once a model is auto-selected.
Read serverStore.props.chat_template directly in non-router mode,
since the global /props already describes the single loaded model.
* server : clear checkpoints upon prompt clear
* server : move the prompt state data to the server_prompt_cache
Assisted-by: pi:llama.cpp/Qwen3.6-27B
* server : handle batched slot being cleared
