llama.cpp/examples/llava

GLMV-EDGE

Currently this implementation supports glm-edge-v-2b and glm-edge-v-5b.

Usage

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

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

./llama-llava-cli -m model_path/ggml-model-f16.gguf --mmproj model_path/mmproj-model-f16.gguf --image img_path/image.jpg -p "<|system|>\n system prompt <image><|user|>\n prompt <|assistant|>\n"

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

GGUF conversion

1. Clone a GLMV-EDGE model (2B or 5B). For example:

   git clone https://huggingface.co/THUDM/glm-edge-v-5b or https://huggingface.co/THUDM/glm-edge-v-2b
   

2. Use glmedge-surgery.py to split the GLMV-EDGE model to LLM and multimodel projector constituents:

   python ./examples/llava/glmedge-surgery.py -m ../model_path
   

3. Use glmedge-convert-image-encoder-to-gguf.py to convert the GLMV-EDGE image encoder to GGUF:

   python ./examples/llava/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path
   

4. Use examples/convert_hf_to_gguf.py to convert the LLM part of GLMV-EDGE to GGUF:

   python convert_hf_to_gguf.py ../model_path
   

Now both the LLM part and the image encoder are in the model_path directory.