candle-llava

LLaVA (Large Language-and-Vision Assistant) is an end-to-end trained large multimodal model. This example is from candle-llava

The code is based on https://github.com/haotian-liu/LLaVA, Hence the llava-hf version of config may perform differently.

model zoo

• liuhaotian/LLaVA
• llava-hf

Right now this has been tested on liuhaotian/llava-v1.6-vicuna-7b and llava-hf/llava-v1.6-vicuna-7b-hf. Memory usage might have room for optimization.

Tokenizer Setup

The llava-hf models contain a tokenizer.json file so can be used directly with the -hf command line flag.

For the original llava models, you can use the following code to generate the tokenizer.json file.

conda create -n llava python=3.10  
pip install transformers protobuf
conda activate llava
python -c "from transformers import AutoTokenizer;tokenizer=AutoTokenizer.from_pretrained('liuhaotian/llava-v1.6-vicuna-7b');tokenizer.save_pretrained('tokenizer')"

Then the tokenizer.json file should be in tokenizer/tokenizer.json (which is the default path).

eval

cargo run --example llava --features cuda -- --image-file "llava_logo.png" --prompt "is this a cat?" --hf # default args, use  llava-hf/llava-v1.6-vicuna-7b-hf. image-file is required^_^
cargo run --example llava --features cuda -- --model-path liuhaotian/llava-v1.6-vicuna-7b  --image-file "llava_logo.png" --prompt "is this a cat?" # use liuhaotian/llava-v1.6-vicuna-7b, tokenizer setup should be done

Major Limitations

1. Currently only support llama-2/vicuna llm. Haven't support Mistral yet.
2. There are some ops like split, nonzero and where are not supported by candle.
3. Lack of quantization and LoRA support.