a00e47e422
* mtmd: add granite-speech support (ibm-granite/granite-4.0-1b-speech) Conformer encoder with Shaw relative position encoding, QFormer projector, log-mel spectrogram with frame stacking. Encoder uses GLU gating, folded batch norm, and SSM depthwise conv. QFormer compresses encoder output via windowed cross-attention (window=15, queries=3) into the LLM embedding space. Audio preprocessing: reflect-padded STFT, 80-bin mel filterbank, dynamic range compression, 2x frame stacking (80->160 mel). GGUF converter handles batch norm folding at export time, fused K/V split, and Conv1d weight reshaping. Tested against HF transformers reference: token-for-token match on 30s/60s audio clips with greedy decoding. * mtmd: rename gs_ prefixed tensors to generic/architecture names * mtmd: use tensor_mapping.py for all granite_speech tensors * convert: fold GraniteSpeechTextModel into GraniteModel * mtmd: replace n_layer hack with explicit has_standard_layers flag * mtmd: replace hardcoded magic numbers with GGUF hparams for granite speech * mtmd: align KEY_A_ define spacing * convert: register GraniteModel for GraniteSpeechForConditionalGeneration * convert: fix ty type-check for GraniteSpeechMmprojModel registration * mtmd: align TN_ define spacing * mtmd: use generic layer loop for granite speech tensor loading * mtmd: merge qformer_proj_layer into clip_layer * mtmd: granite_speech remove redundant ggml_build_forward_expand on inputs * mtmd: granite_speech add comment explaining why build_attn is not used * mtmd: granite_speech hard-code eps in cpp, remove from GGUF metadata * gguf: add spacing between granite_speech tensor mapping blocks * mtmd: make generic audio layer_norm_eps read optional * mtmd: granite_speech keep encoder eps in GGUF, only hard-code projector eps * mtmd: align defines and struct fields in clip-impl.h and clip-model.h * mtmd: fix alignment and ordering issues across granite speech files * convert: granite_speech use filter_tensors instead of modify_tensors for skipping
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