* 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>
* 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>
* remove multimodal code upon maintainer request. Will be made as a separate PR
* Whitespace clean in tensor_mapping.py
* 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
* Update llama-kv-cache.cpp
* Review driven changes
* style fix
* indexer hparams are required
* fix tests
* 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>
- Add a supports_mtp_export capability to ModelBase so architectures can opt
into --mtp and --no-mtp without extending a central class allowlist.
- Enable the capability for the existing Qwen3.5/3.6 and Step3.5/3.7
implementations, and for HY V3, whose converter already supports
filtering the appended MTP layers.
* convert: add dsv4 conversion
* add basic setup
* add llm_graph_input_dsv4
* add save-load state
* add sinkhorn eps - correction by @fairydreaming
* add rope fix
* cleanup dead code
* fix bugs
* support pro model: added by @fairydreaming
* remove redundant V cache
* Chat template
* remove debugging leftovers
* Add mechanism for inlining templates based on architecture
* s/deepseek-v4-flash/deepseek4/g
* s/deepseek-v4-flash/deepseek4/g continued
* enable graph reuse
* enable FA
* fix test llama archs
* rename
* compatibility with antirez ds4 GGUFs
* simplified set_gguf_parameters() by calling super class method, replaced moe.score_func with expert_gating_func.
* reserve worst-case kv-cache
* revert max split inputs
* address review comments
* add padding to enable FA
* pad only the final value of plan.n_kv to 256
* remove built-in cpp chat template
* cont: remove cpp built-in template
* rm outdated test
* replace ggml_view_3d() with ggml_reshape_3d()
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* only support n_seq=1 for now
* remove unused var
* cont: remove unused var
* use scale bias
* use correct ptr for can_reuse
* remove gen-chat-inline-templates.py
* simplify graph reuse
* cont: cleanup
* remove unused inputs
* enable partial checkpointing
* add correct shape for kq_mask + set llama_model_n_swa to 0 for dsv4
* precompute source_idx + add comment about dummy write
* support multi-seq
* remove restored_trim_pos
* use split_equal when possible
* fix indent
* address review comments
* use LLM_KV
* fix ci
---------
Co-authored-by: Piotr Wilkin <piotr.wilkin@syndatis.com>
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: fairydreaming <166155368+fairydreaming@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Add arch support for cohere2-MoE
* Removed redundant gating_func checks
* Changed ffn lookup to prefer prefix_dense_intermediate_size
* Renamed arch to cohere2moe
* Removed redundant lmhead check and chat template changes
* Removed lm_head.weight check from modify tensors, load output tensor not required, fallback to token_embd.weight
* Changed to (routed+shared)*0.5 for shared expert combined avg
* fixed sliding_window_pattern issue and pattern
* Fixed transformers crash 'first_k_dense_replace' error
* Remove comment
* Removed cohere2-moe as a tokenizer type and kept as tiny_aya. Renamed North-Mini-Code-1.0.
* Fixed MTP fail, changed to use iSWA
* Fixed remaining todos: cohere2moe renamed, changed swa parsing to use get_key_or_arr, removed extra get_arr use
* Force metadata usage
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Remove Cohere2 checkpoint comment
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Remove MTP comment
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Regenerate cohere2moe tokenizer hash
* Add cohere2moe to Llama Model Saver supported list
* Check for zerobios tensors and add support for Command to use LayerNorm
* Map expert_selection_fn to sigmoid in base.py instead of command.py
* use bools for foundnorm/foundnormrms
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Add support for the ibm-granite/granite-embedding-{97m,311m}-multilingual-r2 embedding models:
* Added a version of the gpt4o tokenizer that has a fixed regex (better handling of marks), and different token merging setting for the 97m model
* Reused gemma4 tokenizer for the 311m model
* granite-embedding-*-multilingual-r2 : add support SwiGLU FFN for Granite Embedding Multilingual R2
* added new GGUF key <arch>.hidden_activation (LLM_KV_HIDDEN_ACT) + writer
* added a forward declaration of llm_ffn_op_type to llama-hparams.h
* added llm_ffn_op in hparams
* added LLM_FFN_NONE = 0 sentinel to llm_ffn_op_type (value-initialization), modern-bert: explicitly assigns LLM_FFN_GEGLU before reading GGUF (unchanged).
* centralized hidden_act mapping in llama-model.cpp, added llm_ffn_op_type_from_string() helper, mirroring rope_scaling_type/llama_rope_scaling_type_from_string()
* modern-bert reads the GGUF key (when present) and uses the resulting op in its FFN graph
* Added granite-embedding-{97m,311m}-multilingual-r2 to the converter code
* Added the hashes for the granite embedding multilingual R2 models
* Set the hidden_activation in the GGUF if the field is present in config.json (such as for the granite embedding models)
Add minicpm5 pre-tokenizer hash via convert_hf_to_gguf_update.py and
implement hardcoded regex handling in llama-vocab.cpp, consistent with
other BPE pre-tokenizers.
Co-authored-by: zhangtao <zhangtao2@modelbest.cn>
* initial talkie support, coherent
* reorder to follow convention
* absorb inverse rope
* stop folding scalars to improve quantization
* use broadcasting instead of duplication
* style cleanup
* add scaling support to LoraTorchTensor; use that path in conversion
* use layer_out_scale instead of embd_skip_scale
* Refactored Compressed Tensors NVFP4 support for new base.py
* Support compressed-tensors NVFP4 conversion
* Moved Qwen MTP remap into filter_tensors
* simplify
* pathlib no longer used
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* vocab : add Carbon-3B (HybridDNATokenizer) support
Adds a new BPE pre-type LLAMA_VOCAB_PRE_TYPE_CARBON for the
HybridDNATokenizer used by HuggingFaceBio/Carbon-{500M,3B,8B}.
The base BPE is Qwen3-4B-Base's; what differs is that text inside
<dna>...</dna> regions is chunked into fixed 6-mers (right-padded
with 'A' on the trailing partial), and any base outside ACGT maps
to <oov>.
* src/llama-vocab.{h,cpp}: new pre-type, dispatched from
llm_tokenizer_bpe_session::tokenize.
* src/llama-vocab-carbon.h: pure helpers (tokenize_carbon,
emit_dna_kmers) factored out for unit testing — no llama_vocab
dependency, vocab access goes through a std::function.
* conversion/base.py: detect HybridDNATokenizer by class name in
get_vocab_base_pre (chktxt collides with Qwen3 base since it
has no <dna>), and pass trust_remote_code=True in get_vocab_base
so the custom tokenizer class can load.
* tests/test-tokenizer-carbon.cpp: 12 cases covering single 6-mer,
multi 6-mer, lowercase, invalid base -> <oov>, partial k-mer
right-pad, mixed text+DNA, empty <dna></dna>, unterminated <dna>,
two regions, vocab miss.
* vocab : align Carbon-3B changes with llama.cpp conventions
* Fold tokenize_carbon + emit_dna_kmers inline into
llm_tokenizer_bpe_session (drop src/llama-vocab-carbon.h),
matching how every other tokenizer keeps its helpers inside
llama-vocab.cpp.
* Replace the standalone unit test with the conventional
test-tokenizer-0 row backed by models/ggml-vocab-carbon.gguf
(vocab-only conversion) + .inp/.out fixtures covering single
6-mer, multi 6-mer, lowercase, invalid base -> <oov>, partial
right-pad, mixed text+DNA, empty <dna></dna>, unterminated <dna>,
two regions.
* Register "carbon" in convert_hf_to_gguf_update.py's model list
(pointing at HuggingFaceBio/Carbon-3B) and teach both
AutoTokenizer call sites in the updater to pass
trust_remote_code=True for it, matching how t5 is special-cased.
* vocab : move Carbon dispatch to _set_vocab_carbon + LlamaModel branch
Refactor the conversion-side changes to follow the per-tokenizer-family
convention used by _set_vocab_qwen, _set_vocab_interns1, _set_vocab_glm,
etc. instead of conditionalising the shared get_vocab_base /
get_vocab_base_pre paths.
* conversion/base.py: add _set_vocab_carbon — self-contained, loads
with trust_remote_code=True so HybridDNATokenizer's merged Qwen3 + DNA
vocab is visible, writes tokenizer.ggml.pre = "carbon" directly.
* conversion/llama.py: branch in LlamaModel.set_vocab on
tokenizer_config.json["tokenizer_class"] == "HybridDNATokenizer" and
dispatch to _set_vocab_carbon. Same precedent as conversion/bert.py
(tokenizer_class branch between BertTokenizer / RobertaTokenizer) and
conversion/phi.py.
* conversion/base.py: revert the conditional in get_vocab_base and the
class-name short-circuit in the auto-generated get_vocab_base_pre.
* tests : expand ggml-vocab-carbon.gguf fixtures with model-card examples
Add 6 cases from the Carbon-3B model card on top of the existing edge
coverage: the unterminated basic-completion prompt, the closed 33-bp
example, the metadata-conditioned prompt (with <vertebrate_mammalian>
and <protein_coding_region> which BPE-decompose since they are not in
the vocab), the documented anti-pattern of raw DNA without <dna> tags,
and the two likelihood-scoring examples. Brings the suite to 19 cases.
* vocab : promote HybridDNATokenizer to its own LLAMA_VOCAB_TYPE
Refactor per upstream review:
> This should be its own tokenizer model, ie. carbonhybriddna instead
> of gpt2 and not carbon pre-tokenizer. That way you can keep the
> correct pre-tokenizer, in case that ever changes.
Previously the tokenizer was modelled as LLAMA_VOCAB_TYPE_BPE plus a
new LLAMA_VOCAB_PRE_TYPE_CARBON, which (a) put a CARBON-specific
branch inside llm_tokenizer_bpe_session::tokenize (only existing
pre-types differ in regex, not dispatch logic), and (b) conflated
"hybrid DNA tokenization" with "Qwen3 BPE pre-tokenizer".
This change moves it to its own vocab type, peer to PLAMO2, with the
GGUF model name matching the HF tokenizer class (HybridDNATokenizer):
* include/llama.h: new LLAMA_VOCAB_TYPE_HYBRIDDNA = 7.
* src/llama-vocab.cpp: new llm_tokenizer_hybriddna + session that
owns std::unique_ptr<llm_tokenizer_bpe> for non-<dna> text and
routes raw text through a DNA-aware splitter; wired into
init_tokenizer, tokenize, type_name, byte_to_token, and the
BPE-style token_to_piece case (DNA k-mers + <dna>/</dna>/<oov>
are pure ASCII, so byte-level BPE decoding handles them).
LLAMA_VOCAB_TYPE_HYBRIDDNA gets its own branch in the vocab-type
config block alongside SPM/WPM/UGM/RWKV, where pre_type is set
to QWEN2 and the matching add_space_prefix / escape_whitespaces /
clean_spaces flags are applied — mirroring qwen2's BPE path so
byte-level BPE merging stays bit-identical to the Python
reference for non-DNA text.
* src/llama-vocab.h: drop the short-lived LLAMA_VOCAB_PRE_TYPE_CARBON.
* conversion/base.py: _set_vocab_hybriddna writes
tokenizer.ggml.model = "hybriddna" (no separate pre).
* conversion/llama.py: dispatch on tokenizer_class ==
"HybridDNATokenizer" same as bert.py / phi.py do.
* models/ggml-vocab-hybriddna.gguf{,.inp,.out}: renamed fixture +
regenerated metadata.
* convert_hf_to_gguf_update.py: drop the stale chkhsh entry and
trust_remote_code special-case (no longer needed since dispatch
is now class-name driven, not chkhsh).
Verified end-to-end against HuggingFaceBio/Carbon-{500M,3B,8B}:
tokenization is bit-identical to the Python HybridDNATokenizer for
all 19 test fixtures plus the model-card metadata-conditioned
prompt; greedy completion produces the same DNA continuation as
the Python reference; spec-dec with 500M as draft for 8B still
works.
* vocab : relax llm_tokenizer_bpe assert to allow HYBRIDDNA
* vocab : drop llm_tokenizer_bpe vocab-type assert
* vocab : write tokenizer.ggml.pre for HYBRIDDNA, share BPE dispatch
* vocab : assert BPE or HYBRIDDNA in llm_tokenizer_bpe
* vocab : annotate #endif with PRETOKENIZERDEBUG
* vocab : drop local hybriddna fixture (moves to ggml-org/vocabs)
* deduplicate
* simplify
* simplify
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* spec: support MTP
* fix batch size
* rename files
* cont : simplify (#7)
* MTP: clean-up (#9)
* MTP: clean-up
* review: use llama_context_type instead of llama_graph_type
* review: remove llama_model_has_mtp
* review: fix convert issues
* convert: fix pycheck
* review: formatting
* use `mtp-` for identifying mtp models
* convert: fix mtp conversion
* mtp -> draft-mtp
* remove unused llama_arch
* add need_embd in speculative
* llama: allow partial seq_rm for GDN models for speculative decoding
Currently speculative checkpoint needs to restart from a checkpoint
after some draft tokens are not accepted, this leads to some wastage in
running the target again. This PR adds the ability to rollback upto
`draft_max` by storing the GDN intermediates.
* fix pending state
* vulkan: add GDN partial rollback
* meta: extend check to axis 1
* metal: add GDN partial rollback
Extend the gated delta net kernel to store intermediate states for
partial rollback support on the Metal backend.
- Add K (snapshot slot count) as a function constant
- Read input state from slot 0 of the 3D state tensor
- Write intermediate states to different slots during token loop
- For K=1, maintain backward-compatible single-slot behavior
Ref: https://github.com/ggml-org/llama.cpp/commit/8c05923630110223669f069af2000e9cf10c02bc
Assisted-by: llama.cpp:local pi
* delta_net_base: use ggml_pad instead of new_tensor
* review: add need_rs_seq
* review: rename part_bounded to n_rs
* review: deslop comments
* review: rename, add asserts
* server : adjust checkpoint logic (#11)
* server : adjust checkpoint logic
* cont : rm asserts
* server-context: fix early exit
* spec : fix compatibility with n-gram and add TODOs (#13)
* metal : cleanup
* llama : fix faulty bitwise check in recurrent memory
* server : disable RS-based MTP in combination with other spec types
* spec : add TODOs
* cont : fix comment
* cont : update comment
* common : fix logic for ngram + mtp compat
* llama-memory: enable checkpointing with partial rollback
* cont: add test-case for loading into a dirty ctx
* llama-memory-recurrent: clear rs_idx in clear
* download: fix mtp path
* llama-arch: fix enorm op
* docs: update docs
* conversion: fix type annotations
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* move conversion code to a dedicated conversion directory and split the files akin to the src/models architecture
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
