llama.cpp/examples/llava

LLaVA

Currently this implementation supports llava-v1.5 variants, as well as llava-1.6 llava-v1.6 variants.

The pre-converted 7b and 13b models are available. For llava-1.6 a variety of prepared gguf models are available as well 7b-34b

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. note: For GPU offloading ensure to use the -ngl flag just like usual

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

2. Install the required Python packages:

pip install -r examples/llava/requirements.txt

3. 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

4. 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

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

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

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

LLaVA 1.6 gguf conversion

1. Backup your pth/safetensor model files as llava-surgery modifies them
2. Use python llava-surgery-v2.py -C -m /path/to/hf-model which also supports llava-1.5 variants pytorch as well as safetensor models:

• you will find a llava.projector and a llava.clip file in your model directory

3. Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory (https://huggingface.co/cmp-nct/llava-1.6-gguf/blob/main/config_vit.json) and rename it to config.json.
4. Create the visual gguf model: python ./examples/llava/convert-image-encoder-to-gguf.py -m ../path/to/vit --llava-projector ../path/to/llava.projector --output-dir ../path/to/output --clip-model-is-vision

• This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP

5. Everything else as usual: convert.py the hf model, quantize as needed note llava-1.6 needs more context than llava-1.5, at least 3000 is needed (just run it at -c 4096) note llava-1.6 greatly benefits from batched prompt processing (defaults work)

llava-cli templating and llava-1.6 prompting

llava-1.5 models all use the same vicuna prompt, here you can just add your image question like -p "Provide a full description." For llava-1.5 models which are not vicuna (mistral and Yi) you need to adapt system prompt as well as user prompt, for this purpose llava-cli has a basic templating system:

For Mistral and using llava-cli binary: Add this: -p "<image>\nUSER:\nProvide a full description.\nASSISTANT:\n" The mistral template for llava-1.6 seems to be no system print and a USER/ASSISTANT role

For the 34B this should work: Add this: -e -p <|im_start|>system\nAnswer the questions.<|im_end|><|im_start|>user\n<image>\nProvide a full description.<|im_end|><|im_start|>assistant\n

How to know if you are running in llava-1.5 or llava-1.6 mode

When running llava-cli you will see a visual information right before the prompt is being processed:

Llava-1.5: encode_image_with_clip: image embedding created: 576 tokens

Llava-1.6 (anything above 576): encode_image_with_clip: image embedding created: 2880 tokens

Alternatively just pay notice to how many "tokens" have been used for your prompt, it will also show 1000+ tokens for llava-1.6

TODO

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