Kwa Jie Hao
98d2d2884e
* feat: (vocab) fix stray text appended in llama_decode_text Remove accidental concatenation of the full `text` string when formatting UNK_BYTE hex escapes. Only the closing "]" should be appended. * feat(mtmd): add Yasa2 vision encoder support Add a Yasa2 (ConvNeXtV2-based) vision encoder for reka-edge: - Register PROJECTOR_TYPE_YASA2 and tensor name definitions - Add yasa2_block/yasa2_stage model structs - Implement graph builder with ConvNeXt stages, GRN, adaptive pooling - Wire into clip.cpp switch statements and mtmd.cpp init_vision - Use mtmd_image_preprocessor_fixed_size for image preprocessing * feat(chat): add reka-edge template handler (tools, thinking) - Add chat-reka.cpp/h implementing PEG-based parser for reka-edge format - Add Reka-Edge.jinja chat template - Detect reka-edge template in try_specialized_template() - Add LLAMA_EXAMPLE_MTMD to chat-template-file arg * feat: add reka vlm to gguf conversion script Converts Reka Yasa2 hf checkpoints to GGUF format: - Text decoder: Llama-arch with tiktoken/BPE vocab - Mmproj (--mmproj): ConvNeXt vision backbone + language_projection - Generates 2D sincos positional embeddings for vision encoder * test: add Reka Edge chat template and parser tests - test-chat-template: oracle tests comparing Jinja engine output vs common_chat_templates_apply for text, tools, thinking, images, video - test-chat: PEG parser tests for Reka Edge format, round-trip tests for image/video content parts, common path integration tests * scripts: add Reka Edge mixed quantization helper Q4_0 base quantization with Q8_0 override for the last 8 transformer blocks (layers 24-31) via --tensor-type regex. * fix: adapt chat-reka and tests to upstream API - Use autoparser::generation_params (not templates_params) - Add p.prefix(generation_prompt) to PEG parser - Simplify reasoning parser to match LFM2 pattern - Remove image/video oracle tests (unsupported by oaicompat parser; no other multimodal models test this path) * fix: avoid duplicate tensor loading in yasa2 vision encoder TN_YASA_PATCH_W and TN_PATCH_EMBD both resolve to "v.patch_embd.weight", causing the same tensor to be loaded twice into ctx_data and overflowing the memory pool. Reuse the tensors already loaded by the common section. * chore: update image pre-processing settings The reka-edge model depends on the following settings in an older fork of llama.cpp: 1. Fixed square resize 2. BICUBIC 3. add_padding=false In current llama.cpp, this means setting: - image_resize_algo = RESIZE_ALGO_BICUBIC - image_resize_pad = false * chore: remove reka gguf conversion script * chore: remove reka quantization script * chore: remove unnecessary changes from PR scope This commit removes a couple of unnecessary changes for the PR scope: 1. BPE decoder bug fix - this affects reka edge because there's a bug in our tokenization that doesn't represent <think> tokens as special tokens. However this isn't meant to be a thinking model so when run with --reasoning off the edge case does not affect us 2. --chat-template-file support from llama-mtmd-cli - the focus is on llama-server and the reka edge gguf contains the necessary metadata to detect the chat template 3. reka edge oracle test cases - no other model has similar test cases, so I removed it for standardization * chore: remove unnecessary ggml_cast This commit removes unnecessary ggml_cast after updating the reka vlm -> gguf conversion script on hugging face. * chore: remove redundant code * chore: remove unnecessary ggml_cont calls This commit removes all ggml_cont calls except the four that precede ggml_reshape_3d/ggml_reshape_4d. Those are necessary because ggml_reshape recomputes strides assuming contiguous layout and asserts ggml_is_contiguous. Other operations (ggml_mean, ggml_add, ggml_mul etc.) use stride-based indexing and handle non-contiguous inputs correctly and so we are ok to remove ggml_cont for those. * chore: remove unnecessary ggml_repeat calls This commit removes unnecessary ggml_repeat calls because the underlying ops already broadcast automatically. Every ggml_repeat in yasa2.cpp was expanding a smaller tensor to match a larger one's shape before passing both to an elementwise op (ggml_add, ggml_sub, ggml_mul, or ggml_div). This is unnecessary because all four of these ops already support broadcasting internally. * chore: restore ggml_cont needed for cpu operations * refactor: locate reka chat template handler in chat.cpp * chore: remove unnecessary warmup tokens * chore: add code comments on image_resize_pad * chore: remove custom reka parsing code * chore: revert common/chat.cpp * Uncomment debug logging for PEG input parsing --------- Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
Multimodal Support in llama.cpp
This directory provides multimodal capabilities for llama.cpp. Initially intended as a showcase for running LLaVA models, its scope has expanded significantly over time to include various other vision-capable models. As a result, LLaVA is no longer the only multimodal architecture supported.
Important
Multimodal support can be viewed as a sub-project within
llama.cpp. It is under very heavy development, and breaking changes are expected.
The naming and structure related to multimodal support have evolved, which might cause some confusion. Here's a brief timeline to clarify:
- #3436: Initial support for LLaVA 1.5 was added, introducing
llava.cppandclip.cpp. Thellava-clibinary was created for model interaction. - #4954: Support for MobileVLM was added, becoming the second vision model supported. This built upon the existing
llava.cpp,clip.cpp, andllava-cliinfrastructure. - Expansion & Fragmentation: Many new models were subsequently added (e.g., #7599, #10361, #12344, and others). However,
llava-clilacked support for the increasingly complex chat templates required by these models. This led to the creation of model-specific binaries likeqwen2vl-cli,minicpmv-cli, andgemma3-cli. While functional, this proliferation of command-line tools became confusing for users. - #12849:
libmtmdwas introduced as a replacement forllava.cpp. Its goals include providing a single, unified command-line interface, improving the user/developer experience (UX/DX), and supporting both audio and image inputs. - #13012:
mtmd-cliwas added, consolidating the various model-specific CLIs into a single tool powered bylibmtmd.
Pre-quantized models
See the list of pre-quantized model here
How it works and what is mmproj?
Multimodal support in llama.cpp works by encoding images into embeddings using a separate model component, and then feeding these embeddings into the language model.
This approach keeps the multimodal components distinct from the core libllama library. Separating these allows for faster, independent development cycles. While many modern vision models are based on Vision Transformers (ViTs), their specific pre-processing and projection steps can vary significantly. Integrating this diverse complexity directly into libllama is currently challenging.
Consequently, running a multimodal model typically requires two GGUF files:
- The standard language model file.
- A corresponding multimodal projector (
mmproj) file, which handles the image encoding and projection.
What is libmtmd?
As outlined in the history, libmtmd is the modern library designed to replace the original llava.cpp implementation for handling multimodal inputs.
Built upon clip.cpp (similar to llava.cpp), libmtmd offers several advantages:
- Unified Interface: Aims to consolidate interaction for various multimodal models.
- Improved UX/DX: Features a more intuitive API, inspired by the
Processorclass in the Hugging Facetransformerslibrary. - Flexibility: Designed to support multiple input types (text, audio, images) while respecting the wide variety of chat templates used by different models.
How to obtain mmproj
Multimodal projector (mmproj) files are specific to each model architecture.
For the following models, you can use convert_hf_to_gguf.py with --mmproj flag to get the mmproj file:
- Gemma 3 ; See the guide here - Note: 1B variant does not have vision support
- SmolVLM (from HuggingFaceTB)
- SmolVLM2 (from HuggingFaceTB)
- Pixtral 12B - only works with
transformers-compatible checkpoint - Qwen 2 VL and Qwen 2.5 VL (from Qwen)
- Mistral Small 3.1 24B
- InternVL 2.5 and InternVL 3 from OpenGVLab (note: we don't support conversion of
InternVL3-*-hfmodel, only non-HF version is supported ;InternLM2Modeltext model is not supported)
For older models, please refer to the relevant guide for instructions on how to obtain or create them:
NOTE: conversion scripts are located under tools/mtmd/legacy-models