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https://github.com/ggerganov/llama.cpp.git
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3b169441df
* ggml-alloc : v3 (ggml/727) * ggml-alloc v3 ggml-ci * fix ci ggml-ci * whisper : check for backend buffer allocation failures * whisper : avoid leaks when initialization fails * cleanup ggml-ci * style fixes ggml-ci * sync : ggml * update llama.cpp, clip.cpp, export-lora.cpp * update finetune.cpp, train-text-from-scratch.cpp ggml-ci * ggml-backend : reduce alignment to 32 to match gguf and fix mmap --------- Co-authored-by: slaren <slarengh@gmail.com> |
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| .. | ||
| android | ||
| clip.cpp | ||
| clip.h | ||
| CMakeLists.txt | ||
| convert-image-encoder-to-gguf.py | ||
| llava-cli.cpp | ||
| llava-surgery.py | ||
| llava.cpp | ||
| llava.h | ||
| MobileVLM-README.md | ||
| README.md | ||
| requirements.txt | ||
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.
Model conversion
- Clone
llava-v15-7bandclip-vit-large-patch14-336locally:
git clone https://huggingface.co/liuhaotian/llava-v1.5-7b
git clone https://huggingface.co/openai/clip-vit-large-patch14-336
- Install the required Python packages:
pip install -r examples/llava/requirements.txt
- Use
llava-surgery.pyto split the LLaVA model to LLaMA and multimodel projector constituents:
python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b
- Use
convert-image-encoder-to-gguf.pyto 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
- Use
convert.pyto 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.
TODO
- Support non-CPU backend for the image encoding part.
- Support different sampling methods.
- Support more model variants.