Run npm audit fix, then bump the remaining vulnerable packages within
their current majors: storybook 10.6.2, vitest 4.1.12, vite 7.3.7,
@modelcontextprotocol/sdk 1.32.1, dompurify 3.4.17, mermaid 11.17.2,
@vite-pwa/assets-generator 1.0.4 and @tailwindcss/typography 0.5.20.
Override katex to 0.18.11 and postcss-selector-parser to 7.1.6 since
their dependents still pin vulnerable ranges, bump the sharp override
to 0.35.5, and pin @storybook/svelte so npm does not resolve the 11.0
alpha through the addon-svelte-csf peer range.
Storybook stays on 10.6.2 as 10.6.3 is newer than min-release-age.
Assisted by: Claude (Opus 5.5 Medium)
* hex-fusion: support for GELU+MUL, REPEAT+GET_ROWS and BINARY_NX (back-to-back ADD/MUL/...)
* hex-fusion: factor out fusion logic into separate .h and .cpp and update all related code
* hex-fusion: allow for RMS_NORM+MUL fusions for non-contig tensors
* hex-fusion: streamline fusion eligibility checks and cover missing corner cases
* hex-fusion: streamline fusion flow
* hex-opbatch: make n_tensors limit more scalable
* hex-bufs: adjust the trace buff sizing
* hex-glu: cleanup glu-ops interfaces and add kernel_params
* hex-kparams: clean up kparams handling in unary, ssm-conv, and other ops
* hex-kparams: update the rest of the ops PAD,DIAG,... to use kparams
* hex-unary: add support for collapsing dims
* hex-ops: cleanup redundant runtime checks
* hex-ops: consistent use of kernel_type in all kparams
* hex-fusion: remove redundant check in gdn+cpy
* hex-fusion: further streamlining of fusion rules and code
* hex-fusion: cleanup naming
* hex-opfmt: cleanup op formatting
* hex-fusion: bump fusion lookahead to 64
* hex-fusion: fuse SSM_CONV+SILU
* hex-fusion: fix hvx register spills in fused ssm_conv+silu
* hex-fusion: add more GLU fusions
* hex-fusion: remove duplicate checks and use ggml-common helpers
* hex-kparams: refactor kparams into htp-kparams.h/cpp
* hex-fusion: fix binary-nx fusion regression
* hex-kparams: another pass at cleaning up kparams (removing unused, etc)
* hex-kparams: streamling nameing and move _check_ wrappers into .h
* hex-kparams: templetize htp_kparams_check_<op> to reduce code bloat
* hex-ops: de-duplicate op dispatch switches
* hex-kparams: trim down exposed kparams calls
* hex-kparams: simplify type casting of kparams structs
* hex-fence: de-dup fence tensor construction
* hex-opbatch: simplify tensor shape init/checks
* hex-mm: cleanup leftovers of the original QK defines
* hex-fa: fix stack overflow on older npu firmware
* hex-ctx: move dma_caches into ops_ctx
* server: realtime API: send complete after committed audio drains
* server: realtime API: complete a commit at its end row
Completion is triggered when the stream runs dry, which is a timing
proxy: audio appended while the tail drains leaks into the committed
item, a final commit before the drain merges both items, and back to
back commits collapse into one.
mtmd_audio_stream_flush() records the row count at the end of the
flushed audio (mtmd_audio_stream_get_n_rows_flushed), next to the rows
produced so far (mtmd_audio_stream_get_n_rows). The slot keeps a FIFO
of these end rows and completes each commit once every row up to it is
decoded.
---------
Co-authored-by: Pascal <admin@serveurperso.com>
* opencl: add Adreno depthwise conv2d
Assisted-by: OpenCode
* opencl: use local program for depthwise conv2d
Assisted-by: OpenCode
* opencl: use singed type for args
---------
Co-authored-by: Li He <lih@qti.qualcomm.com>
qwen4exp published the hc-wide residual [n_embd, hc, n_tokens] as
t_layer_inp, but the drafter reads layer inputs as [n_embd, n_tokens]
rows: llama_context sizes each embd_layer_inp buffer as n_embd * n_batch
and the DFlash encoder input is n_extract * n_embd wide. Evaluating a
DFlash draft over a qwen4exp target therefore aborts on the first request
in llama_context::extract_layer_inputs.
Collapse the streams with a plain mean before exposing them, as deepseek4
already does for its hyper-connection residual, and build the mean only
for the layers a drafter actually requested.
Assisted-by: pi:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB)
* Multi-column on Intel XMX matrix engines refactored
Multi-column (2 to 80 activation columns) mul_mat_vec_q for reordered K-quant and Q8_0 weights on the Intel XMX matrix engines (int8 DPAS, ESIMD), for the SYCL backend. It targets speculative decoding verification (2 to 8 tokens per step) and short prompt batches (9 to 80 tokens). Three types also use it for a single column (Q2_K, Q3_K, Q8_0).
* Fixed regression bug
real bug found: the first version produced NaN whenever the row count wasn't a multiple of 4
* Removed GGML_TYPE_Q8_0 activation
* rewritten xmx_mul_mat
64-byte alignment and 16-byte pitch check on the activations
* Implemented fused gate/up kernel
One launch computes up * act(gate) for both FFN weights, reading the activations once and applying SWIGLU or GEGLU in the epilogue. It covers up to 16 columns and works for all six XMX types.
* Removed the temporary XMX_NOGLU environment switch
* Q2_K min-sum optimization
Q2_K weight is d·scl·q − dmin·mc, where scl (the 4-bit scale) and mc (the 4-bit min code) apply to each half of a 32-weight group
* Added dispatch check and GRF sweep
tg128 on Q4_K_M goes from 26.67 to 28.23 tok/s on the B70 (+5.9%).
Q6_K is unchanged.
MUL_MAT passes 1727/1727 and MUL_MAT_VEC_FUSION 1361/1361.
Perplexity at batch 1 is 5.2064 with XMX and 5.2058 without.
* Renamed variables
* Added 2 environment variables
GGML_SYCL_XMX_MAX_COLS (default 80, any value 0 or higher) and GGML_SYCL_XMX_GLU_MAX_COLS (default 16, range 0 to 16): the largest batch for the fused gate/up kernel. It is clamped to 16 because that is the most the fused kernel is compiled for, so the variable can only lower it.
* Update esimd.hpp
1. All five quant structs were repeating the same boilerplate, so I moved it into shared pieces.
2. A new helper xmx_term does one DPAS, converts it to float and scales it per row. The four branches are now one or two lines each, and the shared Cb += z * d8r is written once.
The refactor passes all three op tests. MUL_MAT_VEC_FUSION is 1369/1369, MUL_MAT is 2281/2281 (the count includes your uncommitted f32 tests) and MUL_MAT_ID is 937/937.
* nemotron : support non-standard GQA, BPE tokenizer and domyn-small pre-type
* gguf-py : write Metaspace prepend_scheme as GGUF metadata
* vocab : parse metaspace prepend_scheme into enum, rename key
* vocab : use metaspace prepend_scheme in detokenize, add TODOs for other tokenizers
* vocab : use constant for metaspace escaped space
---------
Co-authored-by: Martin Cimmino <martin.cimmino@domyn.com>
* server : support typed content input for rerank endpoint
Assisted-by: Opencode/Qwen3.8 27B
* server : address rerank multimodal review findings
- reject multimodal rerank input when the model has no rerank chat template (images were silently dropped)
- translate handle_media download/base64 failures to 400 instead of 500
- reject negative top_n with 400 instead of letting resize throw
- fix comment typos, redundant comments, indentation and trailing whitespace
- README: document that TEI input accepts the same shape as Jina; drop stray blank line
Assisted-by: opencode
* Trimmed README
---------
Co-authored-by: timothywang21 <timothywang21@users.noreply.github.com>
* sycl: fix CONV_2D wrong results for non-contiguous input
The kernel indexes the `input` as a contiguous [W, H, C, N] tensor, but
supports_op claimed the op for any layout. A non-contiguous (e.g. `CWHN`
permuted) input was read with the wrong element offsets, silently
producing wrong results.
Gate the CONV_2D op on `input` and `kernel` contiguity, matching the
CUDA backend contract, and assert input contiguity in the op.
`test-backend-ops -b SYCL0 -o CONV_2D`: goes from 16 failures to 0,
none.
```text
2006/2006 tests passed
Backend SYCL0: OK
Backend 2/2: CPU
Skipping
2/2 backends passed
OK
```
Assisted-by: pi (unsloth/Qwen3.8-27B-GGUF:Q4_K_M via llama.cpp)
* sycl : CONV_2D cwhn=1 support
Make the SYCL backend compute `GGML_OP_CONV_2D` correctly for `cwhn=1` (CWHN
channel-order) inputs and kernels, so the previously failing test-
backend-ops CONV_2D cases pass on SYCL instead of being skipped or
miscomputed.
- Implement CWHN layout support in `ggml/src/ggml-sycl/conv2d.cpp`
(`ggml_sycl_op_conv2d`).
- Update SYCL `supports_op` for `GGML_OP_CONV_2D` in
`ggml/src/ggml-sycl/ggml-sycl.cpp` to accept both layouts.
Assisted-by: pi (Greatjedi/Qwen3.8-27B-Intel-Arc-Tuned-GGUF via llama.cpp)
* sycl docs : update ops for SYCL
* feat(ui): add Show tool call in progress display setting
Add the showToolCallInProgress key and its checkbox to Settings > Display,
right next to its "always show" counterpart. Upstream dropped a setting of
this exact name in 43b5e63589, folding it into alwaysShowToolCallContent
because the old name promised expansion that outlives the call; this brings
back the narrower, in-progress-only meaning.
The entry is registry-driven, so SETTING_CONFIG_DEFAULT, the dialog field and
the ui_settings admin baseline pick it up without extra wiring. It ships
enabled, mirroring Show thought in progress, and the always-show help text is
reworded so the two toggles read as a pair rather than contradicting each
other.
Assisted by: llama-ui (ggml-org/Qwen3.8-Flash-Next:Q8_0)
* feat(ui): expand tool call blocks while they are in progress
Drive the default expansion of TOOL_CALL_STREAMING and TOOL_CALL_PENDING from
showToolCallInProgress and leave TOOL_CALL, the terminal state, to
alwaysShowToolCallContent. A finished call now folds back on its own once its
result lands, which is exactly how the reasoning block behaves when the
thought completes.
Manual toggling keeps precedence over both defaults in either direction:
isExpanded() only consults the settings while expandedStates holds no entry
for the section.
Client tests pin the truth table for the two toggles, including the automatic
fold-back and both manual overrides.
Assisted by: llama-ui (ggml-org/Qwen3.8-Flash-Next:Q8_0)
* fix(ui): keep running tool calls expanded
A call the agentic loop reports as running now outranks its section type.
exec_shell_command persists an empty result before reading any output, so a
live call is already TOOL_CALL and folded back, which hid the streaming
output the setting is meant to show.
Move the expansion default to isSectionExpandedByDefault so the running-id
rule is covered by a truth table, and test the pending -> finished transition
instead of only the finished state: a block that only ever got mounted
finished cannot prove it folds back on its own.
Assisted by: llama-ui (ggml-org/Qwen3.8-Flash-Next:Q8_0)
* feat(ui): stream the exec command from the first character
The exec parser read its args strictly, so while the model was still
typing the call the block title stayed empty and the body claimed
"Running...". Parse the args partially (the read_file / edit_file /
write_file pattern), keep a terminal cursor while the args JSON is
incomplete, and stand the conversation cwd in for the x-tool-cwd that
only arrives with the result message, so the prompt is there from the
first character. The body stays empty while the args stream, shows
"Executing command..." once the call is executing, and the wrapper
drops its padding for empty phases so the title sits flush with the
frame.
Assisted by: Delta (zai-org/GLM-5.3)
* fix(ui): stream file tool bodies even before a path arrives
write_file and edit_file refused to render until their args carried a
path, so a call that streams its body first (or a same-named tool with
other arg names) showed an empty block. Meta now carries optional
fileName/filePath, path alias reading moves to shared helpers, and the
blocks fall back to the raw args JSON when no known content key is
present.
Assisted by: Delta (zai-org/GLM-5.3)
* fix(ui): keep an empty write_file body instead of the raw args
A call carrying {"content":""} fell through to the raw-args fallback, so
a finished write of an empty file showed the args JSON. The parser now
leaves content undefined when the key is missing and the block tests
!== undefined, so an empty write renders as an empty code block with its
byte count.
Assisted by: DeepSeek-V4.1-Flash (deepseek-ai)
Two quants of one repo pulled each other's draft: the badge read every
--model-draft arg of the repo, so each row showed both drafts.
Assisted-by: DeepSeek-V4.1-Flash-1017
* opencl: add F32-to-I32 ARGMAX
Assisted-by: OpenCode
* opencl: init to 0 to be consistent with cpu
---------
Co-authored-by: Li He <lih@qti.qualcomm.com>
* Add Flint model (FlintForCausalLM). Assisted by Codex
* cleanup
* one more
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* model : add SenseNova U1 understanding support
Add support for the dense SenseNova U1 understanding branch, including
the U1.5-8B-MoT model.
- Add HF-to-GGUF conversion for NEOChatModel, excluding MoT generation
tensors and concatenating temporal and spatial Q/K normalization
weights.
- Register the sensenova_u1 GGUF architecture and custom spatial RoPE
metadata.
- Implement temporal/spatial Q/K normalization with 4-axis RoPE.
- Keep U1-specific text position handling in the model implementation.
- Add synthetic architecture coverage for sensenova_u1.
Only the dense understanding branch is supported; generation
branches are intentionally rejected during conversion.
* conversion : treat one-expert SenseNova U1 configs as dense
* mtmd : add SenseNova U1 vision support
Add conversion support for the SenseNova U1 vision encoder and implement
its CPU mtmd projector graph.
The graph applies patch embedding, GELU, adjacent-pair 2D RoPE, spatial
downsampling, and dense projection for dynamic-resolution image inputs.
* mtmd : fix SenseNova U1 vision token layout
* model : clean up SenseNova U1 RoPE metadata
Replace the hard-coded spatial RoPE key with shared GGUF and LLM_KV
definitions used by conversion, model loading, and architecture tests.
Clarify why SenseNova U1 retains its custom text-position input: U1 uses
[t, 0, 0, 0], while the generic M-RoPE input uses [t, t, t, 0].
* mtmd : simplify SenseNova U1 projector integration
Map the SenseNova U1 dense projection to the standard mm.0 weight and
bias tensors and reuse the shared projector fields in the loader and graph.
Read vision parameters from the checkpoint configuration and remove the
U1-specific tensor names and model fields while preserving its strided
projection, bias, and row-major token layout.
* mtmd : fix SenseNova U1 image positions and attention
* fix spacing
* mtmd : restore generic test input chunks
* mtmd : restore SenseNova U1 integration after upstream merge
* Update conversion/sensenova_u1.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update src/models/sensenova-u1.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* model : remove SenseNova U1 BF16 activation casts
Use standard backend precision handling for QKV, FFN, and output
projections instead of casting inputs based on the output tensor type.
Assisted-by: Codex
* Update src/models/sensenova-u1.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* llama: create new fused op for QSA scoring
* enroll glm5.3-next too
* tests : initialize the remaining tensors in test_kpool_mask
The harness sentinels were left uninitialized, so leftover memory could read as NaN and fail the NaN check.
* ggml: generalize the k-pool mask op into ggml_top_k_mask
The k-pool mask op only covered the pooled QSA selection of qwen4exp
and glm5-next, while the DSA models and the DeepSeek V4 CSA build the
same top-k mask by hand with fill, set_rows and add.
ggml_top_k_mask(mask, top_k, score, blocks, extra) keeps the mask
cells of the live picks and the extra cells, and sets the rest to
-inf. Every row tensor is 4D in the layout of top_k, and the mask is
broadcast to those rows, so a selection can also be made per head.
score, blocks and extra are optional: without score every pick is
live, without blocks a pick is a cell, and extra holds the cells a
row keeps without a pick.
llm_graph_context::build_fused_top_k_mask views the row tensors in
the layout of kq_mask and registers the fused node. The k-pool
models, the DSA attention of build_attn and the DeepSeek V4 CSA mask
use it when the probe enables the op, and keep their current path on
backends without it.
---------
Co-authored-by: Pascal <admin@serveurperso.com>
* ggml-opencl : add IM2COL_3D support
Assisted-by: OpenCode
* opencl: use same type for kernel and host
* opencl: fix for older adreno
---------
Co-authored-by: Li He <lih@qti.qualcomm.com>
* server : forward llama.cpp sampling params on /v1/messages
* server : pass the request through on /v1/messages instead of copying listed keys
* NIT: Change comment from multiline to single line
* server : accept llama.cpp params under extra_params on /v1/messages
* server : remove test case for trivial functionality
* server : show extra_params in the /v1/messages README example
Aman Gupta