README.md

metadata

datasets:
  - Lin-Chen/ShareGPT4V
pipeline_tag: image-to-text

Model

llava-llama-3-8b-v1_1 is a LLaVA model fine-tuned from meta-llama/Meta-Llama-3-8B-Instruct and CLIP-ViT-Large-patch14-336 with ShareGPT4V-PT and InternVL-SFT by XTuner.

Note: This model is in GGUF format.

Resources:

• GitHub: xtuner
• HuggingFace LLaVA format model: xtuner/llava-llama-3-8b-v1_1-transformers
• Official LLaVA format model: xtuner/llava-llama-3-8b-v1_1-hf
• XTuner LLaVA format model: xtuner/llava-llama-3-8b-v1_1

Details

Model	Visual Encoder	Projector	Resolution	Pretraining Strategy	Fine-tuning Strategy	Pretrain Dataset	Fine-tune Dataset
LLaVA-v1.5-7B	CLIP-L	MLP	336	Frozen LLM, Frozen ViT	Full LLM, Frozen ViT	LLaVA-PT (558K)	LLaVA-Mix (665K)
LLaVA-Llama-3-8B	CLIP-L	MLP	336	Frozen LLM, Frozen ViT	Full LLM, LoRA ViT	LLaVA-PT (558K)	LLaVA-Mix (665K)
LLaVA-Llama-3-8B-v1.1	CLIP-L	MLP	336	Frozen LLM, Frozen ViT	Full LLM, LoRA ViT	ShareGPT4V-PT (1246K)	InternVL-SFT (1268K)

Results

Model	MMBench Test (EN)	MMBench Test (CN)	CCBench Dev	MMMU Val	SEED-IMG	AI2D Test	ScienceQA Test	HallusionBench aAcc	POPE	GQA	TextVQA	MME	MMStar
LLaVA-v1.5-7B	66.5	59.0	27.5	35.3	60.5	54.8	70.4	44.9	85.9	62.0	58.2	1511/348	30.3
LLaVA-Llama-3-8B	68.9	61.6	30.4	36.8	69.8	60.9	73.3	47.3	87.2	63.5	58.0	1506/295	38.2
LLaVA-Llama-3-8B-v1.1	72.3	66.4	31.6	36.8	70.1	70.0	72.9	47.7	86.4	62.6	59.0	1469/349	45.1

Quickstart

Download models

# mmproj
wget https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf/resolve/main/mmproj-model-f16.gguf

# fp16 llm
wget https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf/resolve/main/ggml-model-f16.gguf

# int4 llm
wget https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf/resolve/main/ggml-model-int4.gguf

Build environment

1. Build llama.cpp (docs) .
2. Build ./llava-cli (docs).

Chat by ./llava-cli

# fp16
./llava-cli -m ./ggml-model-f16.gguf --mmproj ./mmproj-model-f16.gguf --image YOUR_IMAGE.jpg -c 4096

# int4
./llava-cli -m ./ggml-model-int4.gguf --mmproj ./mmproj-model-f16.gguf --image YOUR_IMAGE.jpg -c 4096

Reproduce

Please refer to docs.

Citation

@misc{2023xtuner,
    title={XTuner: A Toolkit for Efficiently Fine-tuning LLM},
    author={XTuner Contributors},
    howpublished = {\url{https://github.com/InternLM/xtuner}},
    year={2023}
}