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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:

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

Image
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/llava-llama-3-8b-v1_1-mmproj-f16.gguf

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

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

# (optional) ollama fp16 modelfile
wget https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf/resolve/main/OLLAMA_MODELFILE_F16

# (optional) ollama int4 modelfile
wget https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf/resolve/main/OLLAMA_MODELFILE_INT4

Chat by ollama

# fp16
ollama create llava-llama3-f16 -f ./OLLAMA_MODELFILE_F16
ollama run llava-llama3-f16 "xx.png Describe this image"

# int4
ollama create llava-llama3-int4 -f ./OLLAMA_MODELFILE_INT4
ollama run llava-llama3-int4 "xx.png Describe this image"

Chat by llama.cpp

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

Note: llava-llama-3-8b-v1_1 uses the Llama-3-instruct chat template.

# fp16
./llava-cli -m ./llava-llama-3-8b-v1_1-f16.gguf --mmproj ./llava-llama-3-8b-v1_1-mmproj-f16.gguf --image YOUR_IMAGE.jpg -c 4096 -e -p "<|start_header_id|>user<|end_header_id|>\n\n<image>\nDescribe this image<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"

# int4
./llava-cli -m ./llava-llama-3-8b-v1_1-int4.gguf --mmproj ./llava-llama-3-8b-v1_1-mmproj-f16.gguf --image YOUR_IMAGE.jpg -c 4096 -e -p "<|start_header_id|>user<|end_header_id|>\n\n<image>\nDescribe this image<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"

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}
}
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Dataset used to train xtuner/llava-llama-3-8b-v1_1-gguf

Collection including xtuner/llava-llama-3-8b-v1_1-gguf