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vivym
/
llava-baichuan2-7b

Text Generation
Transformers
Safetensors
llava_baichuan2
Model card Files Files and versions
xet
Community

Instructions to use vivym/llava-baichuan2-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use vivym/llava-baichuan2-7b with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="vivym/llava-baichuan2-7b")
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("vivym/llava-baichuan2-7b", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use vivym/llava-baichuan2-7b with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "vivym/llava-baichuan2-7b"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "vivym/llava-baichuan2-7b",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/vivym/llava-baichuan2-7b
  • SGLang

    How to use vivym/llava-baichuan2-7b 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 "vivym/llava-baichuan2-7b" \
        --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": "vivym/llava-baichuan2-7b",
    		"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 "vivym/llava-baichuan2-7b" \
            --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": "vivym/llava-baichuan2-7b",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use vivym/llava-baichuan2-7b with Docker Model Runner:

    docker model run hf.co/vivym/llava-baichuan2-7b
llava-baichuan2-7b
15.1 GB
Ctrl+K
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  • 2 contributors
History: 2 commits
Ming Yang
init
7fa3eb9 over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    18 Bytes
    init over 2 years ago
  • config.json
    1.14 kB
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  • generation_config.json
    285 Bytes
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  • mm_projector.bin

    Detected Pickle imports (3)

    • "collections.OrderedDict",
    • "torch.HalfStorage",
    • "torch._utils._rebuild_tensor_v2"

    What is a pickle import?

    42 MB
    xet
    init over 2 years ago
  • model-00001-of-00008.safetensors
    1.97 GB
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  • model-00002-of-00008.safetensors
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  • model-00003-of-00008.safetensors
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  • model-00004-of-00008.safetensors
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  • model-00005-of-00008.safetensors
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  • model-00006-of-00008.safetensors
    1.93 GB
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  • model-00007-of-00008.safetensors
    1.99 GB
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  • model-00008-of-00008.safetensors
    1.33 GB
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  • model.safetensors.index.json
    18.7 kB
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  • special_tokens_map.json
    548 Bytes
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  • tokenizer.model
    2 MB
    xet
    init over 2 years ago
  • tokenizer_config.json
    703 Bytes
    init over 2 years ago