* 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 <sigbjorn.skjaeret@scala.com>
* - 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 <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: bluebread <hotbread70127@gmail.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
* Check granite hybriid expert count to set type as LLM_TYPE_7B_A1B or LLM_TYPE_1B
* Use feed fwd dim instead of num of experts
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* WIP: add NVFP4 quantization support
* tests
* improve NVFP4 dot product implementation performance and fix bad super call
* typo
* Use nvfp4 kvalues
* vulkan : fix NVFP4 shader compilation by including kvalues_mxfp4 lookup table
* vulcal and perf fixes
* wip
* Fix metal
* fix vulcan
* Rename threshold & fix wrong scale
* Fix MOE
* Shelf backend implementations (CUDA, Metal, Vulkan, arch-specific SIMD)
Remove NVFP4 support from GPU backends and architecture-specific
optimized dot products. These should be added in separate PRs so
backend specialists can review them independently.
Reverted files:
- ggml-cuda: common.cuh, convert.cu, mmq.cu/cuh, mmvq.cu, vecdotq.cuh,
quantize.cu/cuh, mma.cuh, ggml-cuda.cu, fattn-tile.cuh
- ggml-metal: ggml-metal.metal, ggml-metal-device.cpp, ggml-metal-impl.h,
ggml-metal-ops.cpp
- ggml-vulkan: ggml-vulkan.cpp, all vulkan-shaders/*
- ggml-cpu arch: arm/quants.c, x86/quants.c, powerpc/quants.c, s390/quants.c
Core NVFP4 support (type definition, CPU fallback dot product,
quantization, dequantization, conversion) is retained.
* Fix arch-fallback.h: add NVFP4 generic fallback for all platforms
After shelving backend-specific SIMD implementations, the generic
CPU dot product needs to be aliased on ARM, x86, PowerPC, and s390
platforms that previously relied on arch-specific versions.
* quantize: add NVFP4 as a quantization type option
* Fix ggml_fp32_to_ue4m3: handle subnormal values
Previously, values with ue4m3_exp <= 0 were clamped to 0, causing
all small scales to underflow. This made NVFP4 quantization via
llama-quantize produce garbage (PPL = 5.8M) since typical transformer
weights have amax/6.0 in the range 0.001-0.01, which falls in the
UE4M3 subnormal range.
Now subnormals are properly encoded as man * 2^-9 (exp=0, man=1..7),
matching the decode path in ggml_ue4m3_to_fp32.
Result: NVFP4 requantization now produces PPL = 15.25 (vs F16 = 14.33),
comparable to Q4_1 (PPL = 15.81) at slightly lower BPW (4.70 vs 5.15).
* Restore ARM NEON NVFP4 dot product implementation
Restores the optimized ggml_vec_dot_nvfp4_q8_0 for ARM NEON using
vqtbl1q_s8 lookup and ggml_vdotq_s32 dot products.
tg128 performance: 4.37 t/s (generic) -> 13.66 t/s (NEON) = 3.1x speedup
* Optimize ARM NEON NVFP4 dot product: LUT + vpaddq + vfmaq
- Add ue4m3_scale_lut[128] to ggml-common.h replacing branch-heavy
ggml_ue4m3_to_fp32() in the hot loop
- Use vpaddq_s32 for pairwise int32 reduction instead of vaddvq_s32
- Accumulate with vfmaq_f32 into float32x4_t vector accumulators
tg128: 8.1 -> 31.0 t/s (3.8x speedup, 77% of Q4_1 speed)
* ARM NEON NVFP4: rearrange q8 to match nibble layout
Alternative approach: rearrange q8 data to match the NVFP4 lo/hi
nibble layout instead of rearranging the looked-up NVFP4 values.
Eliminates vcombine_s8(vget_low, vget_low) shuffles.
Performance is equivalent (~18.5 t/s) - the bottleneck is the 2x
block overhead from QK=16 vs QK=32, not the shuffle instructions.
* CPU only backend 64 super-block layout
* cleanup
* Remove unused LUT
* int
* exclude NVFP4 from unsupported ops in metal build
* remove quantization for now
* store scales as native UE4M3, preserve original model bits when possible
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* correct comment
* format
* reduce duplication and cleanup
* Address comments
* move detection to prepare_tensors
* Use math instead of const
* Move
* fix comment
* Shelf quantize tests
* Rebase and move check
* cleanup
* lint
* Update gguf-py/gguf/scripts/gguf_convert_endian.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Use fallback quant config
* Simplify
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* organize
* Refactor
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* add quantize_nvfp4 (required for test_quants.py)
* add quantize_nvfp4 (required for test_quants.py)
* add quantize_nvfp4 (required for test_quants.py)
* fix return type
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* llama : add support for Nemotron 3 Super
This commit adds support for the Nemotron 3 Super model (120B.A12B)
enabling this model to be converted to GGUF format and run in llama.cpp.
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Matt Clayton <156335168+mattjcly@users.noreply.github.com>
* tests: add end-to-end tests per model architecture
* fixup for rebase
* fix use-after-free in llama-model-loader.cpp
* fix CI
* fix WebGPU
* fix CI
* disable CI for macOS-latest-cmake-arm64
* use expert_weights_scale only if != 0.0f
* comments
* model : fix Qwen3.5 model type detection
* Update src/llama-model.cpp
whoops, my bad
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* WIP: Add EuroBERT support with autoformatting changes
This commit includes:
- EuroBERT model implementation for GGUF conversion
- C++ backend support for EuroBERT architecture
- Unintended autoformatting changes to Python files
Saving before reverting formatting-only changes.
* feat: add back eos assert when not last token pooling
* feat: removed duplicated code and cleanup
* feat: removed not working architectures and unnecessary check
* fix: typo
* fix: dynamic pooling config
* feat: added an example model for eurobert
* feat: proper llama-vocab implementation for jina-v5
* fix: removed unnecessary comments
* model: add JAIS-2 architecture support
Add support for the JAIS-2 family of Arabic-English bilingual models
from Inception AI (https://huggingface.co/inceptionai/Jais-2-8B-Chat).
Architecture characteristics:
- LayerNorm (not RMSNorm) with biases
- ReLU² (ReLU squared) activation function
- Separate Q/K/V projections with biases
- Simple MLP without gate projection (up -> act -> down)
- RoPE positional embeddings
- GPT-2 BPE tokenizer
Supported model sizes:
- Jais-2-8B (32 layers, 26 heads, 3328 hidden)
- Jais-2-70B (68 layers, 56 heads, 7168 hidden)
Tested with quantizations: BF16, Q8_0, Q6_K, Q5_K_M, Q5_0, Q4_K_M, Q4_0, Q3_K_M, Q2_K
Note: JAIS-2 requires F32 precision accumulators for numerical stability
and uses standard attention (not flash attention) on CUDA backends.
* fix: run convert_hf_to_gguf_update.py for jais-2 tokenizer hash
* fix: use NEOX RoPE type for JAIS2
* fix: remove Q/K permutation (NEOX RoPE doesn't need it)
* fix: enable flash attention for JAIS2 (fixed by #19115)
* fix: add dedicated JAIS2 pre-tokenizer type and control vector support
- Add LLAMA_VOCAB_PRE_TYPE_JAIS2 with cascading whitespace regex
- Include original regex from tokenizer.json as comment
- Add build_cvec call for control vector support
* no longer necessary to override set_vocab
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* model : Add tokenizer from LFM2.5-Audio-1.5B
[LFM2.5-Audio-1.5B](https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B) introduced lightweight audio tokenizer.
Tokenizer based on LFM2 architecture and acts as "embedding" model with
different input `n_embd` and output `n_embd_out`.
To be used in https://github.com/ggml-org/llama.cpp/pull/18641.
To convert use
```shell
python3 convert_hf_to_gguf.py /path/to/LFM2.5-Audio-1.5B/audio_detokenizer
```
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Formatting
* Rework check for attention layers
* Add LFM2 SWA model support
* Address PR feedback
* Set vocab to none
* Move helper function definitions to cpp file
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* full modern bert support
* added gelu op in rank pooling for modern bert
* still working on stuff, added mean calculation before classifier head
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* first layer is dense, as per modern bert research paper
* Update src/llama-graph.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* fixed set input for mean pooling to check if pooling type is ranking since modern bert does mean & rank
* Update src/llama-graph.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
