llama.cpp/examples/llava

LLaVA

Currently this implementation supports llava-v1.5 variants.

The pre-converted 7b and 13b models are available.

After API is confirmed, more models will be supported / uploaded.

Usage

Build with cmake or run make llava-cli to build it.

After building, run: ./llava-cli to see the usage. For example:

./llava-cli -m ../llava-v1.5-7b/ggml-model-f16.gguf --mmproj ../llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg

note: A lower temperature like 0.1 is recommended for better quality. add --temp 0.1 to the command to do so.

LLaVA 1.5

• Clone a LLaVA and a CLIP model (available options). For example:

  git clone https://huggingface.co/liuhaotian/llava-v1.5-7b
  
  git clone https://huggingface.co/openai/clip-vit-large-patch14-336
  

1. Install the required Python packages:

   pip install -r examples/llava/requirements.txt
   

2. Use llava-surgery.py to split the LLaVA model to LLaMA and multimodel projector constituents:

   python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b
   

3. Use convert-image-encoder-to-gguf.py to convert the LLaVA image encoder to GGUF:

   python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
   

4. Use convert.py to convert the LLaMA part of LLaVA to GGUF:

   python ./convert.py ../llava-v1.5-7b
   

Now both the LLaMA part and the image encoder is in the llava-v1.5-7b directory.

LLaVA 1.6

• Use llava-surgery-v2.py

• TODO: add detailed instructions

TODO

• Support non-CPU backend for the image encoding part.
• Support different sampling methods.
• Support more model variants.