* hparams: add per-layer n_ff_exp/n_expert_used arrays with scalar-or-array loading
G1/G2 infrastructure for variable-per-layer expert FFN size and top-k routing
(required for Puzzle-75B which has 5 distinct n_ff_exp values and 7 top-k values
across its 40 MoE layers).
Design: rename scalar members to _impl suffix (following existing convention),
add LLAMA_MAX_LAYERS arrays, add n_ff_exp(il)/n_expert_used(il) accessors with
scalar fallback. No new GGUF keys: reuses existing expert_feed_forward_length and
expert_used_count keys via get_key_or_arr (scalar -> broadcast, array -> per-layer).
- llama-hparams.h: n_ff_exp -> n_ff_exp_impl, n_expert_used -> n_expert_used_impl;
add n_ff_exp_arr / n_expert_used_arr arrays; add per-layer accessor declarations.
- llama-hparams.cpp: implement n_ff_exp(il) and n_expert_used(il); out-of-range
il returns impl safely (shared code, no abort).
- llama-model.cpp: central n_expert_used load changed to get_key_or_arr; derive
impl as max-of-array for validations and backward compat; zero both new arrays;
HunyuanVL override also zeroes n_expert_used_arr.
- llama-graph.cpp: aggregation loop in build_moe_ffn uses hparams.n_expert_used(il)
so per-layer top-k bounds the ggml_view loop correctly.
- All other files: mechanical rename hparams.n_{ff_exp,expert_used} -> *_impl.
Scalar arches are unaffected (broadcast fills all array slots with the single value).
(cherry picked from commit 269a81e03d66e1c353e1a203a0c03a03eb2b1a4e)
* nemotron-h: use per-layer n_ff_exp(il) and n_expert_used(il) at MoE call-sites
Load n_ff_exp via get_key_or_arr into hparams.n_ff_exp_arr in load_arch_hparams;
derive impl as max for existing uniform GGUFs.
In load_arch_tensors, compute n_ff_exp_i = hparams.n_ff_exp(i) with fallback to
n_ff(i)/n_expert_used(i) for GGUFs that omit expert_feed_forward_length.
In build_ffn_layer, pass hparams.n_expert_used(il) to build_moe_ffn so per-layer
top-k is used for expert routing selection.
All other nemotron-h behaviour (mamba2, attention, shared-exp, latent projection,
routed_scaling_factor, expert_weights_norm, sigmoid gating) is unchanged.
(cherry picked from commit b1878a101793cd4e59868ac72635a86ea694987c)
* arch/*.cpp + gguf-py: mechanical rename n_ff_exp->n_ff_exp_impl, n_expert_used->n_expert_used_impl
All non-nemotron arch files continue using the scalar impl member directly.
Behaviour is identical: the impl value is the broadcast value from the GGUF scalar.
gguf_writer: add_expert_feed_forward_length and add_expert_used_count now accept
int | Sequence[int], mirroring add_feed_forward_length, so converters can write
per-layer arrays with the same existing GGUF keys.
(cherry picked from commit 8f009f54bea5ef9a6a354123bd25e9d5ea2d5e03)
* convert: support NemotronHPuzzleForCausalLM (per-block MoE config)
Parse block_configs/mtp_block_configs into per-layer arrays (scalar-or-array
keys), append the MTP [attention, moe] sub-blocks as blk.88/blk.89 with
nextn tensors, accept the backbone.* prefix, and register the arch.
Also fix a pre-existing undeclared _experts attribute on NemotronHModel.
(cherry picked from commit d1a592f278336e78457454eb6c96bca917135f10)
* nemotron-h: distinguish Nemotron 3 Puzzle (75B.A9B) from Super (120B.A12B)
Both have 88 layers; the per-layer expert_used_count array (heterogeneous
for Puzzle, broadcast-uniform for Super) is the discriminator.
(cherry picked from commit f824e09dc812169589cf5662c92d149a4c18c30a)
* convert: accept the official Puzzle BF16 checkpoint's tensor naming
The officially distributed BF16 checkpoint (NVIDIA-Nemotron-Labs-3-Puzzle-
75B-A9B-BF16) names the trunk model.* (model.layers.*, model.embeddings,
model.norm_f) where the original release used the NemotronH-style
backbone.*, and spells the router bias e_score_correction_bias instead of
e_score_correction.bias. Normalize both at the top of
NemotronHPuzzleModel.modify_tensors so either checkpoint converts; every
tensor name in the official index (42683 keys, MTP head included) resolves
through the tensor map after normalization.
(cherry picked from commit 189b67fc2c9d50970416c94b3317a6e7baa49b03)
* laguna: use n_ff_exp_impl for the uniform-MoE FFN size
Laguna landed after this branch was cut and reads hparams.n_ff_exp as a
scalar. This series turns it into a per-layer array with an n_ff_exp(il)
accessor, so the three scalar reads no longer compile. Laguna is a
uniform MoE, so point them at the scalar fallback n_ff_exp_impl, same as
deepseek2/qwen3moe/gemma4 in this series. No behaviour change.
(cherry picked from commit dbedc9e19c50dca0acdfb402362e2707bee424ae)
* arch: extend the n_ff_exp/n_expert_used rename to archs added upstream
kimi-k3, dflash, bailingmoe3, deepseek4, granite-swa and the nemotron-h MTP
block still referenced the scalar fields by their old names. n_ff_exp and
n_expert_used are accessors now, so those reads no longer compile; point the
non-per-layer archs at the _impl scalars and use the indexed form where the
call site is per-layer.
* convert: keep Puzzle opted out of the NemotronH MTP export path
#26725 added MTP export to NemotronHModel, keyed on num_nextn_predict_layers.
Puzzle's config carries that key, but NemotronHPuzzleModel bypasses
NemotronHModel.__init__ (its per-block config needs a different setup), so
_mtp_bid was never assigned and modify_tensors raised AttributeError on any
mtp.* tensor. Puzzle's head is also laid out by mtp_block_configs, not the
mtp.layers.* form the base maps.
Set _mtp_bid to None, drop mtp.* in filter_tensors, and declare
supports_mtp_export = False so --mtp / --no-mtp fail at the CLI.
* llama: replace n_ff_exp/n_expert_used scalars with per-layer accessors
Follow-up to review feedback: the previous revision kept the scalar
hparams fields alongside the new per-layer arrays, which duplicated
state that get_key_or_arr already handles by broadcasting a scalar
value over every layer.
Drop both scalars and expose n_ff_exp(il) / n_expert_used(il) built
exactly like the existing n_head_kv(il) and n_ff(il) accessors: they
index the array and GGML_ABORT out of range, with il defaulting to 0
so genuinely uniform call sites stay a plain n_ff_exp().
Arch loaders now read both keys through get_key_or_arr over
n_layer_all, and the n_expert_used validation checks the maximum
across layers instead of a single field.
* llama: restore per-key required flags on the expert hparam reads
The scalar-to-array conversion passed required=false at every call site,
which silently made mandatory keys optional. Each read now carries the
same required flag it had before the conversion.
* mtmd: llama.cpp DeepSeekOCR support
init commit
* loading sam tensors
* mtmd: fix vision model processing
* deepseek-ocr clip-vit model impl
* mtmd: add DeepSeek-OCR LM support with standard attention
* mtmd: successfully runs DeepSeek-OCR LM in llama-cli
* mtmd: Fix RoPE type for DeepSeek-OCR LM.
* loading LM
testing Vision model loading
* sam warmup working
* sam erroneous return corrected
* clip-vit: corrected cls_embd concat
* clip-vit: model convert qkv_proj split
* corrected combining of image encoders' results
* fix: update callback for ffn_moe_weighted and add callback for attn_out in deepseek2 model
* concat image_newline and image_seperator tokens
* visual_model warmup (technically) works
* window partitioning using standard ggml ops
* sam implementation without using CPU only ops
* clip: fixed warnings
* Merge branch 'sf/deepseek-ocr' of github.com:sfallah/llama.cpp into sf/deepseek-ocr
* mtmd: fix get_rel_pos
* mtmd: fixed the wrong scaler for get_rel_pos
* image encoding technically works but the output can't be checked singe image decoding fails
* mtmd: minor changed
* mtmd: add native resolution support
* - image encoding debugged
- issues fixed mainly related wrong config like n_patches etc.
- configs need to be corrected in the converter
* mtmd: correct token order
* - dynamic resizing
- changes are concerning PR https://github.com/sfallah/llama.cpp/pull/4
* mtmd: quick fix token order
* mtmd: fix danling pointer
* mtmd: SAM numerically works
* mtmd: debug CLIP-L (vit_pre_ln)
* mtmd: debug CLIP-L & first working DeepSeek-OCR model
* mtmd : add --dsocr-mode CLI argument for DeepSeek-OCR resolution control & all native resolution modes work
* mtmd: simplify SAM patch embedding
* mtmd: adapt Pillow image resizing function
* mtmd: simplify DeepSeek-OCR dynamic resolution preprocessing
* mtmd: remove --dsocr-mode argument
* mtmd: refactor code & remove unused helper functions
* mtmd: fix tensor names for image newlines and view separator
* clean up
* reverting automatically removed spaces
* reverting automatically removed spaces
* mtmd: fixed bad ocr check in Deepseek2 (LM)
* mtmd: support combined QKV projection in buid_vit
* using common build_attn in sam
* corrected code-branch when flash-attn disabled
enabling usage of --flash-attn option
* mtmd: minor fix
* minor formatting and style
* fixed flake8 lint issues
* minor editorconfig-check fixes
* minor editorconfig-check fixes
* mtmd: simplify get_rel_pos
* mtmd: make sam hparams configurable
* mtmd: add detailed comments for resize_bicubic_pillow
* mtmd: fixed wrong input setting
* mtmd: convert model in FP16
* mtmd: minor fix
* mtmd: remove tweak to llama-mtmd-cli & deepseek-ocr template
* fix: test-1.jpg ORC issue with small (640) resolution
setting min-resolution base (1024) max large (1280) for dynamic-resolution
* minor: editconfig-check fix
* merge with changes from https://github.com/ggml-org/llama.cpp/pull/17909
added new opt to tests.sh to disable flash-attn
* minor: editconfig-check fix
* testing deepseek-ocr
quick and dirty test script comparing results of Qwen2.5-VL vs DeepSeek-OCR
* quick and (potential) dirty merge with https://github.com/ggml-org/llama.cpp/pull/17909
* refactoring, one single builder function and static helpers
* added deepseek-ocr test to tests.sh
* minor formatting fixes
* check with fixed expected resutls
* minor formatting
* editorconfig-check fix
* merge with changes from https://github.com/ggml-org/llama.cpp/pull/18042
* minor
- added GLM-4.6V to big tests
- added missing deps for python test
* convert: minor fix
* mtmd: format code
* convert: quick fix
* convert: quick fix
* minor python formatting
* fixed merge build issue
* merge resolved
- fixed issues in convert
- tested several deepseek models
* minor fix
* minor
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <[email protected]>
* - removed clip_is_deepseekocr
- removed redundant RESIZE_ALGO_BICUBIC_PILLOW resize-algo
- simplified image-preprocessing
- removed/simplified debug functions
* - cleaning commented out code
* fixing instabilities issues reintroducing resize_bicubic_pillow
* - use f16 model for deepseek-ocr test
- ignore llama-arch test for deepseek-ocr
* rename fc_w --> mm_fc_w
* add links to OCR discussion
* cleaner loading code
* add missing .weight to some tensors
* add default jinja template (to be used by server)
* move test model to ggml-org
* rolling back upscale change
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <[email protected]>
---------
Co-authored-by: bluebread <[email protected]>
Co-authored-by: Sigbjørn Skjæret <[email protected]>
Co-authored-by: Xuan Son Nguyen <[email protected]>
Co-authored-by: Xuan-Son Nguyen <[email protected]>
* mla : pass V as a view of K to the FA op
* cuda : adjust mla logic to new layout
* kv-cache : fix rope shift
* tests : remove comment
* cuda : fix reusable_cutoff
Co-authored-by: Johannes Gäßler <[email protected]>
---------
Co-authored-by: Johannes Gäßler <[email protected]>
* Detect GigaChat3-10-A1.8B as deepseek lite
Hardcodes checking number of layers to detect if lite version of deepseek.
* Add commnent identifying deepseek lite variants
deepseek lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B
Yaniss Amazouz
Sigbjørn Skjæret
Xuan-Son Nguyen
jacekpoplawski
Georgi GerganovandSigbjørn Skjæret
Aman GuptaandSigbjørn Skjæret
Christian Kastner
ubergarm
Piotr Wilkin (ilintar)andSigbjørn Skjæret