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llava : fix clip-model-is-vision flag in README.md (#5509)

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* llava: fix clip-model-is-vision flag in README.md

This commit fixes the flag `--clip_model_is_vision` in README.md which
is does not match the actual flag:
```console
$ python convert-image-encoder-to-gguf.py --help
...
  --clip-model-is-vision
                        The clip model is a pure vision model
                        (ShareGPT4V vision extract for example)
```

Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>

* llava: update link to vit config in README.md

Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>

---------

Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
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Daniel Bevenius 2024-02-16 10:24:39 +01:00 committed by GitHub
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@ -63,8 +63,8 @@ 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)
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`
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)