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e235b267a2
* py : switch to snake_case ggml-ci * cont ggml-ci * cont ggml-ci * cont : fix link * gguf-py : use snake_case in scripts entrypoint export * py : rename requirements for convert_legacy_llama.py Needed for scripts/check-requirements.sh --------- Co-authored-by: Francis Couture-Harpin <git@compilade.net>
140 lines
5.2 KiB
Markdown
140 lines
5.2 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 llama-llava-cli` to build it.
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After building, run: `./llama-llava-cli` to see the usage. For example:
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```sh
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./llama-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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1. 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 `examples/convert_legacy_llama.py` to convert the LLaMA part of LLaVA to GGUF:
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```sh
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python ./examples/convert_legacy_llama.py ../llava-v1.5-7b --skip-unknown
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```
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Now both the LLaMA part and the image encoder are in the `llava-v1.5-7b` directory.
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## LLaVA 1.6 gguf conversion
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1) First clone a LLaVA 1.6 model:
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```console
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git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b
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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_v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models:
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```console
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python examples/llava/llava_surgery_v2.py -C -m ../llava-v1.6-vicuna-7b/
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```
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- you will find a llava.projector and a llava.clip file in your model directory
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4) 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:
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```console
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mkdir vit
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cp ../llava-v1.6-vicuna-7b/llava.clip vit/pytorch_model.bin
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cp ../llava-v1.6-vicuna-7b/llava.projector vit/
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curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.json -o vit/config.json
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```
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5) Create the visual gguf model:
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```console
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python ./examples/llava/convert_image_encoder_to_gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision
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```
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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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6) Then convert the model to gguf format:
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```console
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python ./examples/convert_legacy_llama.py ../llava-v1.6-vicuna-7b/ --skip-unknown
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```
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7) And finally we can run the llava cli using the 1.6 model version:
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```console
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./llama-llava-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf --image some-image.jpg -c 4096
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```
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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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