* feat(convert): Get language model conversion working for 4.1 vision
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(convert): Skip multimodal tensors for GraniteMoeHybrid (vision 4.0)
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Disable vocab padding for non-hybrid models that use GraniteMoeHybrid
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Plumb python-side vision projector names and mappings
There are several awkward things here:
1. Most of these are essentially identical to the audio qformer tensors. On
the c++ side, that's mapped using the prefix, so the rest of the GGUF
name needs to align, but on the python side there's no prefix notion, so
they all get duplicated.
2. There are a couple of net-new tensors for vision, in particular
PROJ_NORM. In both speech and vision, the QF_PROJ_NORM is qualified as
belonging to the qformer portion, but the GGUF name is simply proj_norm
which conflicts with the ideal name for this new PROJ_NORM that is not
qualified as part of the qformer. To get around this, I used
"proj_layernorm" as the GGUF name.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add python side architecture name
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add python-side plumbing for setting FEATURE_LAYERS hparam
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add c++ side tensor naming defines
NOTE: Usage of these hasn't been updated to include prefix yet
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(mtmd): Convert vision_feature_layer to an ordered vector
We need to preserve the ordering of these feature index values so that they
can be mapped to the sub-tensors within the stacked projectors.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(mtmd): Add architecture label plumbing
Branch: Granite4Vision
AI-usage: full (OpenCode + qwen3.5:122b)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(wip): Add partial conversion for mmproj
This handles stacking the projector tensors and setting the new harams
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add gguf_writer and constant support for new hparams and deepstack layer arr
Branch: Granite4Vision
AI-usage: draft (OpenCode + qwen3.5:122b)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Full conversion for mmproj w/ tensor mappings
Branch: Granite4Vision
AI-usage: full (OpenCode + qwen3.5:122b)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Add lm_head skip for mmproj for 4.0
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: De-alias text_config architecture in convert_lora_to_gguf.py
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add --trust-remote-code arg to convert_lora_to_gguf.py
This defaults to False, but allows a user to enable it programmaticly
instead of using the interactive prompt.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: De-alias model.language_model. -> model. for lora adapters
Branch: Granite4Vision
AI-usage: full (OpenCode + qwen3.5:122b)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Extend language model tensor dealiasing in adapters
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove unnecessary registration for GraniteSpeech in language model
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Plumb through mm prefix formatting for qformer tensors
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Refactor vision projector tensors to use predictor ID as the block
This is cleaner than stacking them. The modeling file hard-codes
single-layer qformers, so we can punt on the multiipule multi-layer
projectors problem.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add spatial offests array hparam conversion
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add stub plumbing for granite vision in mtmd
Branch: Granite4Vision
AI-usage: draft (OpenCode + qwen3.5:122b)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add new hparam and tensor naming in clip-impl.h
New hparams:
- KEY_PROJ_SAMPLE_QUERY_SIDE
- KEY_PROJ_SAMPLE_WINDOW_SIDE
- KEY_PROJ_SPATIAL_OFFSETS
New tensors:
- TN_MULTI_PROJ_IMG_POS
- TN_MULTI_PROJ_QUERY
- TN_MULTI_PROJ_LAYERNORM
- TN_MULTI_PROJ_LINEAR
- TN_MULTI_PROJ_NORM
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Move deepstack_layer_arr to llm hparam instead of mmproj
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove IS_DEEPSTACK_LAYERS
This appears to have been added during Qwen3 VL
(https://github.com/ggml-org/llama.cpp/pull/16780), but it was never
actually used.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: n_deepstack_layers -> deepstack_layer_arr
The old logic hard coded a correspondence between the first N layers of the
LLM and the 1->N entries in the input embeddings. Now, that relationship is
maintained at loading time if the GGUF value is single-valued. If it is
multi-valued, it loads directly allowing for deepstack layers to be spaced
out throughout the model.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Use try/catch for single/multi valued deepstack info
The alternative would be to use get_key_or_arr, but then the single value
would be populated through the entire array and we'd need to detect that
and update it with the right correspondence.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add deepstack injection point for granite LLM
The use of ggml_add here assumes that the elements of inp_embd will be pre-
arranged to be the full embedding length with only the vision-mask'ed
portions non-zero from the projector. This matches how Qwen3VL does it.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: add missing vision attn layernorm eps
Branch: Granite4Vision
AI-usage: full (OpenCode + Qwen 3.6-35B)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Hoist qformer tensors into qf_block and hold a vector for multi-proj
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix missing prefix template for TN_QF_PROJ_LINEAR
It's not strictly necessary since vision uses the blockwise version, but it
makes the loading consistent.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Add embedding scale and image grid pinpoints hparams in conversion
Also remove dead parsing for self._deepstack_layer_arr
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add mtmd KEY_ section for hparams shared with the LLM
In this case, we need the EMBEDDING_SCALE so we can unscale the image
embeddings to compensate for applying embedding scale to the input
embeddings
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Implement c++ hparam parsing
Branch: Granite4Vision
AI-usage: draft (Claude Code)
Co-authored-by: Eli Schwartz <eliyahu.schwartz@ibm.com>
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Flatten pinpoints in conversion
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Add missing break
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: No reason to have modality prefix for img_pos
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add tensor loading
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(convert): Fix confusion between proj.norm and proj.qformer.layernorm
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Use the right portion of speech for tensor loading!
Also plumb through the layernorm -> post_norm naming change
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add logging of deepstack_layers_arr if set
I also changed the print_f output type to int32_t to avoid printing
overflow values for -1. This could cause overflows on the other side, but
I can't imagine a value for any of the current array hparams that would
trigger that.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Make sure input embeddings are cont before f_embedding_scale
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add init and mmproj_embd cases for g4v
The n_mmproj_embd is 1+ to make space for the text embedding and all 8
projectors
Branch: Granite4Vision
AI-usage: draft (Bob)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Invert (h, w) -> (w, h) pinpoints
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Reorder projectors based on llm index and skip the first injection
The multi-projector stack has a strange asymmetry based on how it's
currently implemented for qwen3vl: on the mmproj side, it's all N
projectors, but the output of the "first" (by inp_embd index) projector is
automatically consumed as if it were a standard single-projector mmproj,
so the deepstack portion needs to only contain the 1-N entries.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Eli Schwartz <eliyahu.schwartz@ibm.com>
* fix: Fix mmproj hparams in conversion
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Eli Schwartz <eliyahu.schwartz@ibm.com>
* fix: Fix ordering/logic for deepstack injection in granite
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Eli Schwartz <eliyahu.schwartz@ibm.com>
* fix: Fix preprocessing config to match what the model needs
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Eli Schwartz <eliyahu.schwartz@ibm.com>
* wip: Partial port of Eli's implementation
This is still pretty broken, but it's getting closer. It now happily
generates tokens, but the values are quite incorrect still. I suspect it's
caused by the mapping of projectors from safetensors to their respective
orders here.
Also, this implementation breaks encapsulation pretty badly in mtmd_encode.
This will need a big refactor to put the G4V-specific encoding logic
somewhere more appropriate.
Branch: Granite4Vision
AI-usage: draft (Claude Code, Bob)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Eli Schwartz <eliyahu.schwartz@ibm.com>
* fix: Fix the pre-scaling on the input embeddings to correctly invert the scale
We've got tokens! They still don't line up quite right, so something's a
little off, but we're getting much closer now.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: invert embedding multiplier -> base_scale at load
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix setting image_resize_pad after new enum introduced
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Add G4V to mmproj mapping in conversion
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Re-add padding disable for non-hybrid hybrid models
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Simplify G4V n_tokens computation
This is slightly more efficient and flexible for when we implement the
unpad cropping. IMO, it's also clearer that it is adding the number of
image_newline tokens (embeddings) to the grid, rather than recomputing the
entire count.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add new clip APIs for post-tile-encoding assembly
Granite 4 Vision uses llava-next style pack-and-unpad which requires
injecting the learned newline after each row of the tile grid. A row here
is a single row of the grid which is composed of (grid_x * cols_per_tile) *
(grid_y * rows_per_tile), so the result is newlines injected in between
individual tile rows, thus not something that can be handled with the
standard llava-uhd block-wise endcoding.
Branch: Granite4Vision
AI-usage: draft (Claude Code + Opus 4.7)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add model interfaces for granite 4 vision assembler
I'm on the fence about the best organization of this. These free functions
allow the per-architecture logic in clip.cpp to access the model-specific
graph building, but they still require a fair bit of model-specific logic
in clip.cpp which is not ideal.
I think a better approach may be to replicate what is done with the
graph builders themselves (and possibly even make the assembler part of the
model's existing graph builder).
Branch: Granite4Vision
AI-usage: full (Claude Code + Opus 4.7)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Remove all g4v-specific branching from mtmd.cpp in favor of clip assembler
Branch: Granite4Vision
AI-usage: full (Claude Code + Opus 4.7)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor(mtmd): Consolidate assembler logic into clip_assembler class family
Just like `clip_graph` is the base class for building the model-specific
encoder graphs, `clip_assembler` will be the base class for building the
model-specific assembler graphs. This allows the assembly pattern to follow
how the encoder pattern is implemented where the model-specific logic lives
in a subclass co-located with the encoder graph builder that gets
constructed by a simple factory method.
Branch: Granite4Vision
AI-usage: full (Claude Code + Opus 4.7)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* style: Comment improvement
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: granite_vision -> granite4_vision
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove dead codepath for Qwen3VL add_vision_is_deepstack
These pieces were never used on the c++ side (removed there in an earlier
commit), so this is just cleanup that I missed before.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Oops! I did not mean to commit one of my prompt files
But now it's too far back in history to effectively rebase out, even with
interactive and --rebase-merges :(
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Add missing <algorithm> include for std::find
It seems that this was already pulled in on some platforms, but not on
others
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix Flake8 warnings in granite conversion module
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Remove clip_assembler in favor of clip_image_f32.append_token
Per conversation in the PR, the clip_assembler pattern was too invasive.
This is a compromise that limits model-specific blocks to add_media where
each preprocessed tile is annotated with an injection type, after which all
the token counting logic is generic and the newline injection itself is
handled in the graph based on the value for the given tile image.
Branch: Granite4Vision
AI-usage: draft (Bob, OpenCode + Qwen 3.6 35b)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor(convert): Split n_deepstack_layers and deepstack_layers (array)
Branch: Granite4Vision
AI-usage: full (Bob, OpenCode + Qwen3.6-35b)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor(src): Handle n_deepstack_layers and deepstack_layers GGUF keys
Branch: Granite4Vision
AI-usage: draft (Bob, OpenCode + Qwen3.6-35b)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix GGUF key for deepstack_layers_arr
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Remove pre-scaling embeddings and skip scaling for raw embd inputs
This follows how gemma3 and gemma4 handle embedding scaling by skipping the
multiplier for raw input embeddings.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: deepstack_layers(_arr) -> deepstack_mapping(_arr)
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Fully revert changes to n_deepstack_layers and qwen3vl*
Since we're going to keep the GGUF KVs separate, it makes sense to just
keep the hparams separate too to limit the scope of this branch. The down
side is that n_deepstack_layers and deepstack_mapping_arr are potentially
conflicting.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Revert removal of "is_deepstack_layers" GGUF KV
This KV is not used at all on the c++ side, so it's fully dead, but there's
also no need to conflate this cleanup with the addition of G4V.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove unnecessary ggml_cont and build_forward_expand in cbx
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* style: Clean up comments
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Tighter and more flexible code for g4v_build_block
This could be refactored to look a lot more like granite-speech, but the
overall block constructs before/after the qformer are pretty different, so
for now I'm going to leave it as is and just tighten a bit.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove unnecessary `unordered_set` include
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Add architecture guard on deepstack_mapping_arr printout
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove unnecessary AI-gen comment
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Always initialize deepstack_mapping_arr with -1 values
This was causing `test-llama-archs` to fail, likely due to trying to save
the uninitialized values, then re-loading them. It's safer to always
initialize so that other models don't forget and end up with undefined
behavior.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* style: Remove TODO about block/vs non-block tensor mapping
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Move is_vision_feature_layer logic into clip_hparams
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Use a bool for append_token
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* style: Remove unnecessary comment
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove unused get_model api
yikes!
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Rearrange helpers for g4v to be private members and use build_attn
Branch: Granite4Vision
AI-usage: full (Bob, OpenCode + Qwen3.6-35b)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix off-by-one in vision layer index
This was inherited from the Claude Code implementation that pushed the
negative index inversion down into the model file.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix norm/post_norm mixup in conversion
face. palm. :(
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* style: More descriptive tensor names
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Apply PR cleanup for new conversion changes
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* fix(convert): Remove duplicate V_ENC_EMBD_IMGNL
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: append_token -> add_newline
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* style: Comment cleanup
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Cleaner error handling/checking
NOTE: format_string is not available in granite.cpp (and including
clip-impl.h to get it doesn't compile, so I think it violates the intended
encapsulation), so std::stringstream is the simplest answer.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* 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
- HunyuanOCR shares the same HF arch and vision layout as HunyuanVL butwas split into a separate path that skipped the +0.1 bilinear sampler used by the HF reference.
- Collapse OCR into the HUNYUANVL projector + HUNYUAN_VL text arch
* add qwen3a
* wip
* vision ok
* no more deepstack for audio
* convert ASR model ok
* qwen3 asr working
* Apply suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* nits
* Apply suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* fix bad merge
* fix multi inheritance
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* mtmd : add MERaLiON-2 multimodal audio support
Adds support for A*STAR's MERaLiON-2 audio-language model (3B and 10B)
to the multimodal framework.
Architecture:
- Whisper large-v2 encoder for audio feature extraction
- Gated MLP adaptor: ln_speech -> frame stack (x15) -> Linear+SiLU -> GLU -> out_proj
- Gemma2 3B / 27B decoder
The mmproj GGUF is generated via convert_hf_to_gguf.py --mmproj on the full
MERaLiON-2 model directory (architecture: MERaLiON2ForConditionalGeneration).
The decoder is converted separately as a standard Gemma2 model after stripping
the text_decoder. weight prefix.
New projector type: PROJECTOR_TYPE_MERALION
Supports tasks: speech transcription (EN/ZH/MS/TA), translation, spoken QA.
Model: https://huggingface.co/MERaLiON/MERaLiON-2-3Bhttps://huggingface.co/MERaLiON/MERaLiON-2-10B
* simplify comments in meralion adaptor
* meralion: use format_tensor_name, ascii arrows in comments
* feat: support step3-vl-10b
* use fused QKV && mapping tensor in tensor_mapping.py
* guard hardcoded params and drop crop metadata
* get understand_projector_stride from global config
* img_u8_resize_bilinear_to_f32 move in step3vl class
* Apply suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* fix the \r\n mess
* add width and heads to MmprojModel.set_gguf_parameters
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
This commit adds a missing comma in the vision encoder attention qkv
block.
The motivation for this change is that without the comma there will be
a string concatenation of the Kimi-K2.5 and the Nemotron Nano v2 VL
tensor mappings which will be broken.
* 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>
* 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>
* 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>
* Move dequant_model to after the text_config merge
Add new kimi-k2.5 keys to mtmd convert
Update V_MMPROJ tensor mapping for new mm_projector.proj keys
Update V_M_IMP_NORM for new mm_projector.pre_norm key
* Fix a couple of oversights
* Add image support for Kimi-K2.5
* Revert changes to KimiVLForConditionalGeneration
* Fix an assert crash
* Fix permute swapping w / h on accident
* Kimi-K2.5: Use merged QKV for vision
* Kimi-K2.5: pre-convert vision QK to use build_rope_2d
* Kimi-K2.5: support non-interleaved rope for vision
* Kimi-K2.5: fix min / max pixel
* Kimi-K2.5: remove v/o permutes, unnecessary
* Kimi-K2.5: update permute name to match
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Kimi-K2.5: replace build_rope_2d ggml_cont with ggml_view_3d pointers
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* support qwen3.5 series
* remove deepstack for now, and some code clean
* code clean
* add FULL_ATTENTION_INTERVAL metadata
* code clean
* reorder v heads for linear attention to avoid expensive interleaved repeat
* Unified delta net handling
* Remove old methods.
* Refactor and optimize
* Adapt autoregressive version from @ymcki
* Change to decay mask approach
* Fix bad permute
* Qwen 3.5 support
* Apply suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Further fixes
* Use inheritance, remove unneeded conts
* Not like this!
* Remove ggml.h explicit import
* Remove transformers, fix the views
* ACTUALLY fix views, make super calls explicit in conversion.
* Fix conversion again
* Remove extra ggml.h imports
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* kimi linear model implementation
* kimi linear convert_hf_to_gguf
* kimi linear constants.py tensor_mapping.py
* Kimi Linear ggml.h
* kimi linear ggml-cpu
* Kimi Linear ggml-cuda
* Kimi Linear ggml.c
* kimi linear src/llama
* remove "const int64_t n_seq_tokens = q->ne[2];" to get rid of unused variable warning
* remove type mismatch warning
* read MoE params
* removed some hard coded code
* removed all hard code
* use DeepseekV2 tokenizer
* removed unnecessary internal methods called by the old set_vocab of KimiLinear
* rewrite get_vocab for KimiLinear. Removed all kda_scan code
* removed all traces of kda_scan
* reduce OP count by 1 due to removal of kda_scan
* Move KIMI_LINEAR to llm_arch_is_hybrid to enable KV cache
* set n_embd_head_k/v to ensure kv cache works
* don't quantize conv1d of Kimi Linear
* Kimi Linear backend agnostic
* removed LOG_INFO
* naive chunking form implemented
* fixed some comments
* add Kimi-K2 specific tokens to be recognized as EOG
* build_kda_autoregressive is implemented to replace build_kda_recurrent for faster inference. sync'd to b7682
* replaced Akk and Aqk with mul_mat and clamp
* no clamp version
* Moved Aqk computation out of the loop
* fixed typo and split wkv_b into wk_b and wv_b
* MLA KV cache support
* fix trailing spaces
* moved const llama_model & model; around to follow qwen3next format and see if it cna pass the -Wunused-private-field error
* fix trailing whitespace
* removed traling whitespaces in empty line + make sure indentation is multiple of 4
* try to make lint happy
* remove blank lines to make lint happy
* removed at least blank line containing white space
* fixed flake8 complaints locally
* return ggml_tensor * pair in kda_autoregressive and kda_chunking as in ngxson's Qwen3Next improvement
* removed Kimi-Linear specific change that causes failure at server-windows
* removed private: from kimi_linear to make build checks happy
* removed unnecessary ggml_cont before ggml_reshape
* created static function causal_conv1d to abtract similar code for q/k/v
* merged dt_bias to SSM_DT. Do -exp(log_A) in convert_hf_to_gguf.py.
* reverted to original
* fixed find_hparam calls. Fixed e_score_correction_bias to use bias instead of weight. Removed all ssm_conv bias terms.
* remove DT_B from constants.py. remove one comment line in llama-model.cpp
* new class llm_graph_input_mem_hybrid_k to get around the new MLA change. switch the concat order of ggml_concat calls in kimi-linear.cpp to accommodate MLA changes. Removed support for exp_probs_b.weight
* remove ssm_o_norm_b
* remove ssm_o_norm_b
* changed hparams.kda_head_dim to hparams.n_embd_head_kda. added TODO comment for class llama_graph_mem_hybrid_k
* removed all ggml_cont b4 ggml_reshape_4d
* Whitespace
* replaced all hparams.get with find_hparams
* added new names for n_experts, n_experts_used and score_func in TextModel and removed their code in KimiLinear in convert_hf_to_gguf.py. Removed unnecessary ggml_cont and GGML_ASSERT in kimi-linear.cpp
* use is_mla to switch between different mem_hybrid types
* fixed logical errors in convert_hf_to_gguf.py pointed out by CISC
* removed if else for required parameters kv_lora_rank and qk_rope_head_dim
* add back ggml_cont for Vcur
* minor changes
* removed extra line in llama-vocab.cpp. Added back the comment in llama-graph.cpp
* f16 gguf cannot run without context length
* made a mistake of adding back n_ctx parsing
---------
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
* Add Gemma3nVisionModel - MobileNetV5 vision encoder convertor to convert_hf_to_gguf.py. Add gemma3n to vision projectors in gguf-py/gguf/constants.py.
* Add mobilenetv5 impl
* Fix comments, remove unused vars
* Fix permute and remove transpose of projection weights
* Fix comments, remove debugging prints from hf_to_gguf
* 1. Hard-code image_mean = 0 and image_std = 1
2. Use available tensor mapping logic
3. Remove redundant chat template replacement of soft tokens placeholder with media placeholder
* 1. Move mobilenetv5 helpers declarations to `clip_graph_mobilenetv5` struct and definitions to mobilenetv5.cpp
2.Remove unused `clip_is_gemma3n` func declarations and definitions
3. Remove redundant `rescale_image_u8_to_f32` func and use `normalize_image_u8_to_f32` with zero mean and unit std
4. Calculate n_patches using image_size / patch_size
* Remove obsolete comments
* - convert_hf_to_gguf.py & constants.py & tensor_mapping.py: Use explicit mapping: Custom map for double indexed blocks and tensor_mapping.py for rest
- convert_hf_to_gguf.py: Unsqueeze Stem Bias and Layer scale tensors to correct shape while converting to gguf
- mobilenetv5.cpp: Remove explicit reshaping of Stem Bias and Layer scale which are now handled while converting to gguf, replace fprintf with LOG_*
- clip.cpp: Remove unused embedding and hard_emb_norm tensor loading
* - Rename tensors to v.conv..., v.blk..., v.msfa... to better align with already existing terminology
* Fix stem conv bias name
* Remove explicit handling of bias term for stem conv
* - Change order of addition in "project_per_layer_inputs" to support broadcasting of vision inp_per_layer
- Simplify the vision embeddings path of "get_per_layer_inputs" to output [n_embd_altup, n_layer, 1], broadcastable
* clean up conversion script
* fix code style
* also preserve audio tensors
* trailing space
* split arch A and V
* rm unused gemma3 func
* fix alignment
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
ModernBERT but without `head.norm` so will currently fail to convert and run any other ModernBERT models, PRs with `head.norm` support welcome!
* constants and tensor mappings for modern bert support, model not supported yet but working on getting conversion to work for encoder only
* conversion now working, hf -> gguf
* working on support, now working on building graph
* some cleanup
* cleanup
* continuing
* correct tensor shape for qkv
* fixed tensor mappings and working on buildin graph
* tensor debugging now works -> (llama-eval-callback), instead of simulated gate split with views, GEGLU is now used which does exactly this
* cleanup
* cleanup
* cleanup
* more cleanup
* ubatch issues, the assert for checking equal seqs in llama-graph.cpp when building attention keeps failing, setting ubatch size to 1 when running llama-embedding with --ubatch-size 1 makes it work, but needs to be looked into more
* added cls token per previous modern bert attempt, still working on checking out the rest
* fixed pre tokenizer and still working through previous pr
* working through previous attemp, implimented more accurate conversion per previous attempt, added local sliding window attention that alternates every third layer
* fixed pre tokenizer
* working on swa with local and global alternating attention
* some cleanup and now fails on build attn
* starting to work, and some cleanup, currently failing on last layer construction in graph build
* alternating rope implemented and modern bert graph build succeeds
* fixed asser for equal ubatch seq
* cleanup
* added mask check in vocab
* fixed alternating rope, the hparams.rope_freq_base_train and hparams.rope_freq_base_train_swa were the same and i set them to correct values
* reuse variable
* removed repeat
* standard swa method can be used instead of a new enum being LLAMA_SWA_TYPE_LOCAL
* correct swa layer indexing, is supposed to be 0, 3, 6 ... instead of 1, 4, 7 ...
* more modular hparam setting
* replaced attn out norm with ffn_norm and cosine similarity between hf embds and llama.cpp embds went way up, from 0.05 to 0.24, replaced the cacheless kv with swa todo per the previous conversion
* Update gguf-py/gguf/tensor_mapping.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update convert_hf_to_gguf_update.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-vocab.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update gguf-py/gguf/tensor_mapping.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 gguf-py/gguf/tensor_mapping.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update gguf-py/gguf/tensor_mapping.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 gguf-py/gguf/tensor_mapping.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update gguf-py/gguf/tensor_mapping.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update gguf-py/gguf/tensor_mapping.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update gguf-py/gguf/tensor_mapping.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update gguf-py/gguf/tensor_mapping.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update gguf-py/gguf/tensor_mapping.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-graph.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-arch.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* removed redundant hparam set
* enums for model sizes
* conversion for modern-bert model supported rather than just granite-small
* Update src/llama-model.cpp
Co-authored-by: Gabe Goodhart <ghart@us.ibm.com>
* Update src/llama-model.cpp
Co-authored-by: Gabe Goodhart <ghart@us.ibm.com>
* fixed ordering of enum for freq_base_swa
* fixed where I added residual, now gives much much better embeddings~
* readded cacheless logic
* removing whitespace
* conversion now working for swa pattern - dense every n layers
* modern bert put into seperate src file
* removing whitespace
* fixed whitespace and newline errors in editorconfig job
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* better naming convention, n_swa_pattern -> swa_period
* reusing sliding_window_pattern key rather than making new dense_every_n_layers key, and adding writing and reading support
* fixing pyright type-check fail
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-hparams.h
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model-saver.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/models/modern-bert.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/models/modern-bert.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/models/modern-bert.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/models/modern-bert.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/models/modern-bert.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model-loader.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model-loader.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model-loader.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* added descriptions in llama-model
* fixed tensor mappings for conversion
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* mapping name for size
* nits
* unused
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Gabe Goodhart <ghart@us.ibm.com>
* convert ok
* no deepstack
* less new tensors
* cgraph ok
* add mrope for text model
* faster patch merger
* add GGML_ROPE_TYPE_MRNORM
* add support for metal
* move glm4v do dedicated graph
* convert: add norm_embd
* clip: add debugging fn
* working correctly
* fix style
* use bicubic
* fix mrope metal
* improve cpu
* convert to neox ordering on conversion
* revert backend changes
* force stop if using old weight
* support moe variant
* fix conversion
* fix convert (2)
* Update tools/mtmd/clip-graph.h
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* process mrope_section on TextModel base class
* resolve conflict merge
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama : add support for NVIDIA Nemotron Nano 3
This commit adds support for the NVIDIA Nemotron Nano 3 model, enabling
the conversion and running of this model.
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Qwen3 Next - cleaned up version
* Whitespaces and stuff
* Correct minor errors
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Misc. fixes.
* Clean up code, add missing hybrid qualifier
* Did someone transpose the SOLVE_TRI result matrix? Perhaps...
* Whitespace
* Proper tensors for cb calls
* Use llama-graph.h vertical alignment
* BROKEN: chunking
* Set new tensors as inputs.
* Proper chunk logic
* It's the circle of life...
* More shenanigans for n_seq > 1
* Nail in the coffin?
* Fix Windows build
* Eh, one fails on Windows, the other fails on Mac... just use general capture.
* quant : cleanup
* model : cleanup
* qwen3 : cleanup
* cont : cleanup
* cont : cleanup
* ggml : revert change
* qwen3 : cleanup
* cont : cleanup
* Readd cmath
* qwen3 : fix typo
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Usual suspects
* fix my bad suggestion
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Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Added GGUF mappings for CogVLM model
* Add tensor mapping for CogVLM visual encoder
* Add CogVLM to conversion script, no vision part yet
* Added CogVLM vision model to conversion script
* Add graph for CogVLM CLIP model
* Add graph for CogVLM
* Fixes for CogVLM. Now compiles.
* Model now runs
* Fixes for cogvlm graph
* Account for graph context change after rebase
* Changes for whitespace
* Changes in convert script according to comments
* Switch CogVLM LLM graph to merged QKV tensor
* Use rope_type variable instead of direct definition
* Change CogVLM CLIP encoder to use SWIGLU
* Switch CogVLM CLIP to use merged QKV
* Apply rebase edits and remove ggml_cont call that is now unnecessary
* clean up
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Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Max Krasnyansky