* mtmd : add LFM3-VL and LFM3-Omni video input
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Assisted-by: Claude Opus 5.5
* mtmd : reserve the soundtrack command to avoid a GCC 11 warning
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* model : add LiquidAI/LFM3-Audio-2B and LiquidAI/LFM3-Omni-2B
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Assisted-by: Claude Opus 5.5
* convert : read the LFM3-Audio config.json as released
A transformers that knows lfm3_audio rewrites the config into another layout (no lfm/encoder keys).
Assisted-by: Claude Opus 5.5
* mtmd : disable flash attention for the LFM3-Audio depthformer
Assisted-by: Claude Opus 5.5
* tts : add a voice option for LFM3-Audio and LFM3-Omni
The model card offers a female and a male voice; --tts-voice for llama-tts, "voice" for the server /tts.
Assisted-by: Claude Opus 5.5
* clean up template handling
* convert : detect LFM3-Audio and LFM3-Omni by model_type
Assisted-by: Claude Opus 5.5
* move banned tokens --> logits bias --> mtmd_helper
* leave mtmd-cli unchanged
* no more template_asr_system
* left over
* llama : keep the LFM3-Audio encoder convs and codec unquantized
Assisted-by: Claude Opus 5.5
* common, mtmd : shorten comments
Assisted-by: Claude Opus 5.5
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* model : add LiquidAI/LFM3-1.7B and LiquidAI/LFM3-VL-2B
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
Co-authored-by: Xuan-Son Nguyen <son@huggingface.co>
Assisted-by: Claude Opus 5.5
* tests : set the SWA rope base of the LFM3 test model
Assisted-by: Claude Opus 5.5
* llama : enable recurrent state rollback for LFM3
Assisted-by: Claude Opus 5.5
* Apply batched suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
Co-authored-by: Tarek Dakhran <github@dakhran.com>
* gguf-py : align the lfm3vl mapping comments
Assisted-by: Claude Opus 5.5
* llama : fix CRLF line endings from the review suggestions
Assisted-by: Claude Opus 5.5
* tests : add the LFM3 Metal fusion baseline
Assisted-by: Claude Opus 5.5
* llama : keep the LFM3-VL depthwise conv kernels in F32 when quantizing
Assisted-by: Claude Opus 5.5
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
Co-authored-by: Xuan-Son Nguyen <son@huggingface.co>
* model : add DeepSeek-V4.1-Flash (deepseek41)
* deepseek41 : remove MXFP8, convert the Engram table to BF16
* deepseek41 : compute the compressed-KV read layers once in the cache
* deepseek41 : use get_cells() for the shared-history check, drop llama_kv_cache::seq_pos_tok_le
* deepseek41 : gather the Engram rows in-graph like qwen4exp, drop the host gather
* deepseek41 : keep the Engram hash constants in hparams.ple_*, one module per Engram layer
* deepseek41 : drop the LLAMA_DSV41_KV_MIRROR toggle
* deepseek41 : keep il last in build_hc_mixes
* deepseek41 : sort the top-k rows before the sparse gather
* tests : add deepseek41 to the Metal fusion baseline
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Add Soprano-80M and Soprano-1.1-80M (https://huggingface.co/ekwek/Soprano-1.1-80M)
as an audio generation model: a Qwen3 backbone plus a Vocos decoder that runs
as a gen-only mmproj.
- convert: detect Soprano checkpoints and export the backbone as qwen3 and the
decoder.pth Vocos weights as the mmproj
- vocab: add the soprano pre-tokenizer (lowercase, whitespace collapse,
per-digit split, [UNK] fallback)
- mtmd: add the Vocos graph and ISTFT, allow loading a gen-only mmproj for
Soprano and include the gen-audio context in mtmd_get_memory_usage
- mtmd-helper: add the Soprano pipeline; the decoder runs over the whole
utterance, so stream mode sends the audio in one chunk after flush()
- tts: document Soprano usage
Assisted-by: Codex
Assisted-by: Claude Opus 5.5
* 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>
* 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>
* mtmd: add audio out stream api
* add /tts endpoint
* add docs
* wire up mtmd_helper_model_can_chat
* upload speaker_ref via form-data
* expose seed to api
* fix header for pcm
* wip
* fix penalty_last_n
* fix server_output_limits
* fix pocket-tts stream
* wip
* add server_schema for tts
* add server routing
* server: coerce seed and stream from multipart form fields
Form fields arrive as strings, so -F seed=42 and -F stream=true were
rejected by the schema. seed joins the integer fields, parsed as a long
long so a full uint32 seed fits, and stream is read as a boolean when
it comes as a string.
---------
Co-authored-by: Pascal <admin@serveurperso.com>
* model: add Muse Spark Open (text, vision and audio)
Muse Spark Open is a latent space MoE: the residual stream stays at 8192
while the 256 experts live in 4096, with a projection down and up around
them, top-8 sigmoid routing with a correction bias. Attention interleaves
three sliding layers at a 2048 window with rope for one global layer
without rope, with a sigmoid output gate and a weightless QK-norm.
Residuals are norm preserving, their gate is folded at conversion time.
Vision reuses the Muse Glimmer tower. Audio is a causal convolutional
pyramid with no attention and no spectrogram, four stages folding 16, 6,
5 and 4 samples into the channel dim, one embedding per 80 ms of 24 kHz.
The quantizer keeps the latent projections and the attention gate at
Q8_0, a little over 1% of the weights, since the whole residual stream
passes through them and the low-bit types turned the output into a
repeating loop. The model saver writes the post norm epsilon, and the
arch is registered in test-llama-archs.
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Muse Spark: drop the redundant forward expand
* Muse Spark: drop the redundant forward expand and tighten the comments
* Update src/llama-quant.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update src/llama-graph.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update conversion/muse_spark.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update conversion/muse_spark.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update conversion/muse_spark.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update conversion/muse_spark.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* model : add LiquidAI/d1-omni-600M decision model
Assisted-by: Claude Opus 5.5
* mtmd : keep conformer GLU sigmoid on CUDA
Assisted-by: Claude Opus 5.5
* server : take d1omni audio through images and input_audio, scope memory-less lfm2 to non-causal
Assisted-by: Claude Opus 5.5
* common : rename decision type d1omni to lfm2-d1-omni, server : make images an alias of files
Assisted-by: Claude Opus 5.5
* Rebase GLM-Next support onto master, and migrate to llama-memory-hybrid-idx
* Add initial MTP support
* Merge branch optimizations. Reduce allocated compute buffer size, speed up long context decode, fla, and slight MTP improvements.
* Review driven changes, remove env vars, protect tensors
* Strip MTP for initial PR
* Clean up after mtp strip
* Clean up after mtp strip
* Update speculative.cpp
* Update llama-context.h
* Clean up after mtp strip
* Fix tokenizer ignore merges
* Improve quantization protection selection
* Refactor mhc helpers, graph base
* Lint Fixes
* Apply suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Skip glm5-next in model saver, fix CRLF
* Skip glm5-next in sweep
* Remove T4 fallback
* Review cleanup
* Review suggestions
* Defer separate MTP gguf handling to MTP PR, drop filter
* Repad n_head_kv
* kpool init apply
* Order by descending score
* Drop guard
* read kpool from hparams, clarify kpool cache flags, remove kpool_build_state(nullptr)
* Add glm5-next support to model saver and add arch test fixture
* Review cleanup
* Kpool pooled caching clarify
* Add multi stream support
* Finish Rebase
* Sparse FA fir DSA prefill
* Const
* Update llama-model.cpp to fix rebase error
* gguf-py : merge tensor map entries for HC tensors
* model : use build_gdn_l2_norm in GLM5_NEXT implementation
* chore : remove trailing whitespace
* model : use new OP precision setting API in GLM5_NEXT implementation
* mtmd : use ggml_swiglu_clamp in GLM5V and apply the image token limit
The two clamps around swiglu_split are what ggml_swiglu_clamp already does,
so the clamp bounds collapse back to one value. GLM5V also never called
set_limit_image_tokens(), so --image-max-tokens had no effect.
Assisted-by: Claude Opus 5
(cherry picked from commit 46d18e12d422be4cc04a70e4a9a9e0168bb3d5b7)
* llama : keep the GLM5-Next k-pool layout across ubatches
The layout was rebuilt from a full cell scan on every ubatch. Pools are fixed
by the positions relative to the sequence's first one, so the layout now lives
on the memory and a ubatch only appends to it.
A sequence edit no longer stales every pooled key either, only the ones at or
after the edited position, which makes a tail seq_rm free. The pooling subgraph
is built unconditionally so the graph shape no longer changes every kpool
tokens, and the pool axis is folded into rows before soft_max, which otherwise
exceeds the CUDA gridDim.y limit past n_kv 262144.
Assisted-by: Claude Opus 5
(cherry picked from commit 5d1c40b93e17fddbf73b785efe43e0d02ccb3977)
* model : write the GLM5-Next recurrent rollback checkpoints
The conv state and the delta net state were only written to the live row, so a
rollback restored whatever the checkpoint rows happened to hold. Take the same
route as kimi-k3: build_recurrent_attn for the state, and write all K_rs conv
groups. That also drops a state view that assumed contiguous rows.
Enroll the arch in test-recurrent-state-rollback, which catches this under its
garbage-filled cache pass.
Assisted-by: Claude Opus 5
(cherry picked from commit 5ace37e86d5d448e83ef5dde5632c748185b18cd)
* llama: fix PR #27773 test-save-load-state restore failure
Clear the attention and indexer cache data after a failed hybrid state restore so restored NaNs cannot affect a later sequence.
Assisted-by: Codex
* llama: fix PR #27773 gpu-rocm graph reallocation
Reserve the full GLM5-Next pool capacity and dirty pool count. The gpu-rocm Test step aborts when n_new grows while the graph node count stays fixed; CUDA, Vulkan, Metal, and WebGPU checks report the same error.
Assisted-by: Codex
* llama : fix GLM5-Next k-pool layout staleness after edits and shared teardown
Two defects in the cross-ubatch k-pool layout added by the k-pool commit:
1. Wrong results. An edited sequence only rebuilt its pool layout when its cell
count changed, so if the first ubatch after an edit added back exactly as many
cells as were removed, the stale position-to-cell list survived. With a unified
cache and more than one sequence, where another sequence takes the freed cells,
the reused layout points at the wrong cells (CPU: large logit drift, CUDA: NaN).
Rebuild whenever the sequence is stale, not only on a size mismatch.
2. Slowdown. "shared" mode was assumed to end only with an edit that forces a
rebuild, but sharing also ends when the other sequence is removed. The survivor
kept shared = true, pinning cache_safe off and re-pooling every pool on every
ubatch (server trigger: n>1 completions with -kvu, via the seq_cp in
copy_state_to). In seq_rm, if the layout has shared cells, stale every sequence
so one rebuild re-derives sharing and cache_safe returns to 1.
Assisted-by: Claude Opus 5
* llama : fix build_attn_mha stream stride for non-contiguous q
build_attn_mha split the batch into streams with a stream stride of
q->nb[3]/n_stream. That only equals one stream's span, (ne[2]/n_stream)*nb[2],
when q is contiguous. GLM5-Next is nope-only, so it does not concat a rope part
and passes the permuted q_absorbed straight in, where nb[3] != ne[2]*nb[2]; the
stride was then n_head times too large and every stream s >= 1 read another
head's queries. Split-KV (-np N without --kv-unified) multi-stream prefill was
wrong for every stream past the first. Unified KV and decode were unaffected
(n_stream == 1, and decode takes the gather path). Other MLA models concat rope
so q is contiguous and the computed value is unchanged for them.
Compute the stride from the token dimension, which is identical for a
contiguous q.
Assisted-by: Claude Opus 5
* llama : re-derive GLM5-Next k-pool sharing on state_read/state_drop
The shared-cell teardown added to seq_rm (stale every sequence when the layout
has shared cells, so a survivor does not keep shared = true and pin cache_safe
off) was missing from the other paths that can free shared cells: state_read
and state_drop staled only the one sequence. Apply the same re-derivation there
and correct the comment that claimed sharing ends only via an edit or seq_rm.
Assisted-by: Claude Opus 5
* quant : drop duplicate GLM5-Next hc_ filter
The hc_ name filter was listed twice in the GLM5_NEXT protection block.
Assisted-by: Claude Opus 5
* glm5-next: scope K-pool cache access to indexed operations
* glm5-next: keep K-pool access in hybrid index memory
* glm5-next: keep mHC graph builders model-local
* glm5-next: mark only touched pools per ubatch
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Piotr Wilkin <ilintar@gmail.com>
* model : fold Ling 3.0 VL into the BailingMoeV3 architecture
Assisted-by: Scout
* model : keep shared NORM rope list intact when gating bailingmoe3 on mrope sections
---------
Co-authored-by: aetherbird <aetherbird@users.noreply.github.com>
* mtmd : mark context as const in more methods
Mark `mtmd_context` as `const` in:
- mtmd_bitmap_init_lazy
- mtmd_tokenize
- mtmd_tokenize_from_parts
- mtmd_helper_support_video
- mtmd_helper_bitmap_init_from_file
- mtmd_helper_bitmap_init_from_buf
- mtmd_helper_video_init
- mtmd_helper_video_init_from_buf
- mtmd_helper_model_can_chat
The tokenization functions in particular are useful to have marked
`const`, as that allows more easily telling the compiler that we can
safely tokenize from multiple threads (`mtmd_tokenize` is already
documented as thread-safe, this just reifies that in the signature).
* mtmd : mark tokenization input pointer as const
Mark the `bitmaps` and `parts` pointers in `mtmd_tokenize` and
`mtmd_tokenize_from_parts` as `const`. This allows more easily calling
these with immutable arrays / vectors.
* mtmd : mark llama_context as const in mtmd_helper_model_can_chat
* feat: add --mmproj-device arg & backwards compatible MTMD_BACKEND_DEVICE env var
* feat: load mmproj device backend immediately, add -mmdev shortflag
* fix: its a pointer now get the name
* clean up
* gen docs
* nits
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* adapt the api
* text model ok
* working impl, need verify and clean up
* mtmd: build the pocket-tts transposed convolutions as GEMM + col2im
ggml_conv_transpose_1d has no grouped mode, so the depthwise upsample
was built as one convolution and one concat per channel, which floods
the graph with small nodes and makes kernel launches dominate the
decoder.
Fold both cases into the column form the seanet decoder already needs:
the general case reshapes the kernel to [IC, K * OC] and matmuls it
with the input, the depthwise case batches a matmul over the channels
so a step scales its own kernel. A single col2im_1d then scatter-adds
the columns back to the signal, with the same shape as before, so the
overlap-add tail, the streaming state and the bias are untouched.
Generation time per frame drops by 80% on CUDA and by 50% on CPU. The
output matches the previous implementation sample for sample, with a
correlation of 0.999994 and identical frame counts.
* flow_temp + frames_after_eos
* chunking
* mtmd: carry the remaining pocket-tts per-pack settings
The language packs also tune the end-of-speech padding and the padding
of short prompts, next to the temperature already carried in the
mmproj: french_24l asks for 8 tail frames instead of the guessed 3,
english_2026-01 asks for short prompts to be padded with spaces.
Write both in the mmproj as clip.gen.audio.frames_after_eos and
clip.gen.audio.pad_short_text, keyed on the pack in the conversion
script like the temperature. The loader keeps them optional, so a
mmproj without them behaves as before. Map semicolons to commas for
every pack instead, the reference only asks for it on three of them and
it costs nothing elsewhere.
Existing mmproj files must be converted again to carry the two keys.
On a long french text the port now lands within 2% of the reference:
22.96s against 23.44s, with the same peak level and the same amount of
silence.
* clip.gen.audio.model_variant
* clean up code comments
* nit: drop the dead flow_temp hparam, the pack table holds the default
* update docs
* address security problems
* less invasive base.py
* lint
* add mtmd_gen_inp_default
* add docs
* rm gen_flow_temp
---------
Co-authored-by: Pascal <admin@serveurperso.com>
* Get started with Onyx
* Add architecture
* Skip keys handled in super()
* Loading tensors
* Shorten
* Graph
* Apply suggestion from @pcuenca
* Remove norm now embedding in transformers weights
* Add eot
* Explicit output_multiplier
* Handle post_norm_eps
* No super call; unhardcode eot.
The pattern `self._set_vocab_gpt2()` seems preferred throughout the
codebase, and it allows `set_vocab()` to be called from a different part
of the Python class hierarchy: the drafter model converter that we may
need eventually.
* Register for drafting
* DFlash: inherit rope type from the linked target.
Another option would be to store it in the gguf file itself.
* mmproj conversion
Note: some fields to be renamed after the implementation works. We are
keeping compatibility with the reference Meta gguf for testing purposes.
* "clip" header declarations
* Load mmproj
* Pre-processing
* Graph
* Go back to using delimiters.
Otherwise our generations are worse.
Transformers does not use them. We need to trace inputs to verify
whether they are equivalent.
* downsample_factor -> merge_size
* Add vision graph
lol, forgot from a previous commit
* Additional renames, align with llama.cpp / transformers
* Prefer _size instead of independent _h and _w
* Fix token layout
Co-authored-by: Young Han <younghan@fb.com>
* onyx: bring the chat parser onto the onyx branch
common/chat.cpp on this branch has no Onyx handling, so a converted model
serves malformed chat: the assistant preamble leaks into content
("to=self<|message|>...") and tool calls fail with
HTTP 500 "The model produced output that does not match the expected
peg-native format"
common_chat_params_init_onyx exists on onyx-fair-patch, added there by
8bb73dd3d. It was never on this branch, so this is not a regression --
the two lines developed independently.
The code here is taken verbatim from that commit. It is the clean side of
`git merge origin/onyx-fair-patch`: chat.cpp is one of the files that
merges without conflict. The full merge is not viable -- it produces 13
conflicts, including add/add on conversion/onyx.py and src/models/onyx.cpp
where the q_norm-folding and metadata-scale approaches contradict each
other, and #4/#7 are stacked on this branch's side of that.
Verified on this branch: builds with 0 errors, converts an Onyx checkpoint,
and serving it gives "4" for "What is 2+2?" plus a correct
get_weather {"city":"Paris"} tool call, where the unported branch gives the
two failures above.
No converter or runtime changes are included, so this should not interact
with the q_norm work.
Co-authored-by: Beto de Paola <betodepaola@meta.com>
* Less params, bilinear pos-emb interpolation as a graph op instead of CPU
* Map to symbolic V_MMPROJ instead of strings
* Make a couple params explicit
* Patchify via build_inp()
* No param for rope_theta
* Small cleanup
* Restore blank line
* Unpermute, to adapt to the latest transformers checkpoint
* Apply norm after token embeddings
This follows the latest transformers approach.
* Remove duplicated function
* build_vit
* onyx: use the model rope theta on sliding-window layers
* DFlash: conversion from transformers drafter
* Revert rope_type derivation from target
NOTE: this breaks compatibility with Meta's distributed DFlash GGUFs, as
the Q/K are stored in "NEOX" (rotated half) format, like in
transformers.
* Apply suggestion from @pcuenca
* Set model type
* Remove comment that will become obsolete
* Hardcode post_norm_rms_eps instead of new param
* Derive SWA+RoPE pattern from gguf array or scalar
* Fix model type <-> number of layers
* Reorder
* Rename
* Fix typo
* DFlash: seed the draft KV cache from multimodal embedding batches
`common_speculative_impl_draft_dflash::process()` returned early on any batch carrying embeddings, so an image prefill never had its target-layer features fused through the DFlash encoder and injected into the draft's KV cache. That left a hole spanning the image's positions, and the next injection at a post-image position failed to initialize its batch:
```
decoding image batch 1/1, n_tokens_batch = 256
decode: failed to initialize batch
llama_decode: failed to decode, ret = -1
process: llama_decode(ctx_dft) failed rc=-1 (n_tokens=17, offset=0)
srv decode: failed to process speculative batch
```
Every image request with `--spec-type draft-dflash` failed with HTTP 500. Text-only was unaffected, since those batches carry token ids and were let through.
Restore the earlier condition, which admits a batch that is either tokens or embeddings and skips only the degenerate neither/both cases. The rest of `process()` is already layout-agnostic -- it gathers features via `llama_get_embeddings_layer_inp()` and indexes `batch_in.pos[]` / `batch_in.seq_id[]`, none of which assume token ids -- so this is the whole fix.
Validated against `muse-glimmer-30B-bf16.gguf` + `mmproj-muse-glimmer-30B-bf16.gguf` + a DFlash draft head, on an image describe-the-shapes request:
- before: HTTP 500, `failed to process speculative batch`
- after: HTTP 200, draft acceptance 0.34012 (167 accepted / 491 generated), mean len 3.04
Output equivalence holds, which is the property that matters: at temperature 0 the drafted response is byte-identical to the same request served with no draft attached (1213/1213 chars), so the draft is drafting correctly through the image context rather than merely not crashing.
* Conversion: prefer rewrite to mapping
* Revert "Conversion: prefer rewrite to mapping"
This reverts commit a92d0ac584d315e876741e85b6dad3dbc8b23bf7.
* fix lint
* sliding_window metadata is not optional
* disable state save/load
* Apply suggestion from @pcuenca
---------
Co-authored-by: Young Han <younghan@fb.com>
Co-authored-by: Beto de Paola <betodepaola@meta.com>
Co-authored-by: Daniel Han <michaelhan2050@gmail.com>
Co-authored-by: ruanrms <ruanslv@gmail.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* 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>
* 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>
* mtmd: fix silent prompt truncation on embedded NUL
mtmd_input_text carried the prompt as a bare const char* with no
length, so a NUL byte in message content cut the prompt at the
tokenizer boundary and dropped every later message plus the assistant
marker, with no log. Add an explicit text_len and thread it through,
matching llama_tokenize and the text only path.
* cleanup
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* mtmd: deepseek-ocr v1 multi-tile dynamic resolution + unified image-preprocessors for both versions (ds-ocr v1 and v2)
* remove hacky API
* fuse row into a long image
* almost working
* adapt to new preprocessor api
* rm debugging printf
* improve
* mtmd: dsocr-tiles fixes (#25481)
* ds-ocr img-preproc fuse_row tile-drop fix for multi rows and columns images
* mtmd drop the duplicate redundant img_end
* deepseekocr graph simplify CLS broadcast cleanup
* test-deepseek-ocr: relax v1 single-view tolerance; drop trailing prompt space; make DRY opt-in and n_predict model-specific (#25486)
---------
Co-authored-by: Saba Fallah <10401143+sfallah@users.noreply.github.com>
Co-authored-by: Saba Fallah <sabafallah@gmail.com>
Tarek DakhranandXuan Son Nguyen
Aman GuptaandDaniel Han
Hans
tc-mb
Maphist0andSigbjørn Skjæret
Takamichi Ohara
Kartik Gulia
Terrence Zhao
Tobyandaetherbird
Mads Marquart
John-Henry LimandXuan Son Nguyen
Eric HartfordandEric Hartford
