mirror of
https://github.com/ggerganov/llama.cpp.git
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60ed04cf82
* 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>
107 lines
4.5 KiB
Markdown
107 lines
4.5 KiB
Markdown
# LLaVA
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Currently this implementation supports [llava-v1.5](https://huggingface.co/liuhaotian/llava-v1.5-7b) variants,
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as well as llava-1.6 [llava-v1.6](https://huggingface.co/collections/liuhaotian/llava-16-65b9e40155f60fd046a5ccf2) variants.
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The pre-converted [7b](https://huggingface.co/mys/ggml_llava-v1.5-7b)
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and [13b](https://huggingface.co/mys/ggml_llava-v1.5-13b)
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models are available.
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For llava-1.6 a variety of prepared gguf models are available as well [7b-34b](https://huggingface.co/cmp-nct/llava-1.6-gguf)
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After API is confirmed, more models will be supported / uploaded.
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## Usage
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Build with cmake or run `make llava-cli` to build it.
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After building, run: `./llava-cli` to see the usage. For example:
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```sh
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./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
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```
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**note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so.
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**note**: For GPU offloading ensure to use the `-ngl` flag just like usual
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## LLaVA 1.5
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- Clone a LLaVA and a CLIP model ([available options](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)). For example:
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```sh
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git clone https://huggingface.co/liuhaotian/llava-v1.5-7b
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git clone https://huggingface.co/openai/clip-vit-large-patch14-336
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```
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2. Install the required Python packages:
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```sh
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pip install -r examples/llava/requirements.txt
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```
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3. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
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```sh
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python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b
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```
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4. Use `convert-image-encoder-to-gguf.py` to convert the LLaVA image encoder to GGUF:
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```sh
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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
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```
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5. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF:
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```sh
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python ./convert.py ../llava-v1.5-7b
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```
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Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` directory.
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## LLaVA 1.6 gguf conversion
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1) Backup your pth/safetensor model files as llava-surgery modifies them
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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:
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- you will find a llava.projector and a llava.clip file in your model directory
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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.
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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`
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- 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
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5) Everything else as usual: convert.py the hf model, quantize as needed
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**note** llava-1.6 needs more context than llava-1.5, at least 3000 is needed (just run it at -c 4096)
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**note** llava-1.6 greatly benefits from batched prompt processing (defaults work)
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## llava-cli templating and llava-1.6 prompting
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llava-1.5 models all use the same vicuna prompt, here you can just add your image question like `-p "Provide a full description."`
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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:
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**For Mistral and using llava-cli binary:**
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Add this: `-p "<image>\nUSER:\nProvide a full description.\nASSISTANT:\n"`
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The mistral template for llava-1.6 seems to be no system print and a USER/ASSISTANT role
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**For the 34B this should work:**
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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`
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## How to know if you are running in llava-1.5 or llava-1.6 mode
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When running llava-cli you will see a visual information right before the prompt is being processed:
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**Llava-1.5:**
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`encode_image_with_clip: image embedding created: 576 tokens`
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**Llava-1.6 (anything above 576):**
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`encode_image_with_clip: image embedding created: 2880 tokens`
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Alternatively just pay notice to how many "tokens" have been used for your prompt, it will also show 1000+ tokens for llava-1.6
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## TODO
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- [x] Support non-CPU backend for the image encoding part.
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- [ ] Support different sampling methods.
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- [ ] Support more model variants.
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