From 4453456c5ac1cc86cda890851a58964f073ec22b Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Thu, 15 Feb 2024 15:41:37 +0100 Subject: [PATCH] llava: update link to vit config in README.md Signed-off-by: Daniel Bevenius --- examples/llava/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/llava/README.md b/examples/llava/README.md index acde4220e..57eb42932 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -63,7 +63,7 @@ Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` director 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.json) +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