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Duplicated from  Mantis-VL/mfuyu_1.5_8b_8192_720p

TIGER-Lab
/
Mantis-8B-Fuyu

Text Generation
Transformers
Safetensors
English
fuyu
multimodal
mfuyu
mantis
lmm
vlm
Model card Files Files and versions
xet
Community

Instructions to use TIGER-Lab/Mantis-8B-Fuyu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use TIGER-Lab/Mantis-8B-Fuyu with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="TIGER-Lab/Mantis-8B-Fuyu")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoProcessor, AutoModelForCausalLM
    
    processor = AutoProcessor.from_pretrained("TIGER-Lab/Mantis-8B-Fuyu")
    model = AutoModelForCausalLM.from_pretrained("TIGER-Lab/Mantis-8B-Fuyu", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use TIGER-Lab/Mantis-8B-Fuyu with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "TIGER-Lab/Mantis-8B-Fuyu"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "TIGER-Lab/Mantis-8B-Fuyu",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/TIGER-Lab/Mantis-8B-Fuyu
  • SGLang

    How to use TIGER-Lab/Mantis-8B-Fuyu with SGLang:

    Install from pip and serve model
    # Install SGLang from pip:
    pip install sglang
    # Start the SGLang server:
    python3 -m sglang.launch_server \
        --model-path "TIGER-Lab/Mantis-8B-Fuyu" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "TIGER-Lab/Mantis-8B-Fuyu",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker images
    docker run --gpus all \
        --shm-size 32g \
        -p 30000:30000 \
        -v ~/.cache/huggingface:/root/.cache/huggingface \
        --env "HF_TOKEN=<secret>" \
        --ipc=host \
        lmsysorg/sglang:latest \
        python3 -m sglang.launch_server \
            --model-path "TIGER-Lab/Mantis-8B-Fuyu" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "TIGER-Lab/Mantis-8B-Fuyu",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use TIGER-Lab/Mantis-8B-Fuyu with Docker Model Runner:

    docker model run hf.co/TIGER-Lab/Mantis-8B-Fuyu
Mantis-8B-Fuyu
18.8 GB
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  • 1 contributor
History: 6 commits
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DongfuJiang
Update README.md
ea76e74 verified over 2 years ago
  • .gitattributes
    1.57 kB
    Duplicate from MFuyu/mfuyu_1.5_8b_8192_720p over 2 years ago
  • README.md
    3.97 kB
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  • config.json
    826 Bytes
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  • generation_config.json
    115 Bytes
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  • model-00001-of-00004.safetensors
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  • model-00002-of-00004.safetensors
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  • model-00003-of-00004.safetensors
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  • model-00004-of-00004.safetensors
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  • model.safetensors.index.json
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  • preprocessor_config.json
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  • special_tokens_map.json
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  • tokenizer.json
    16.5 MB
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  • tokenizer_config.json
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  • training_args.bin
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