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Lin-Chen
/
ShareCaptioner-Video

Text Generation
Transformers
PyTorch
internlmxcomposer2
conversational
custom_code
Model card Files Files and versions
xet
Community
4

Instructions to use Lin-Chen/ShareCaptioner-Video with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Lin-Chen/ShareCaptioner-Video with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Lin-Chen/ShareCaptioner-Video", trust_remote_code=True)
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("Lin-Chen/ShareCaptioner-Video", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use Lin-Chen/ShareCaptioner-Video with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Lin-Chen/ShareCaptioner-Video"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Lin-Chen/ShareCaptioner-Video",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/Lin-Chen/ShareCaptioner-Video
  • SGLang

    How to use Lin-Chen/ShareCaptioner-Video 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 "Lin-Chen/ShareCaptioner-Video" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Lin-Chen/ShareCaptioner-Video",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    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 "Lin-Chen/ShareCaptioner-Video" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Lin-Chen/ShareCaptioner-Video",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use Lin-Chen/ShareCaptioner-Video with Docker Model Runner:

    docker model run hf.co/Lin-Chen/ShareCaptioner-Video
ShareCaptioner-Video
17.2 GB
Ctrl+K
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  • 3 contributors
History: 3 commits
AdinaY's picture
AdinaY HF Staff
Add paper link to connect the model to the paper on Daily Papers page
ffd354b verified almost 2 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 2 years ago
  • README.md
    2.69 kB
    Add paper link to connect the model to the paper on Daily Papers page almost 2 years ago
  • added_tokens.json
    158 Bytes
    init almost 2 years ago
  • build_mlp.py
    7.23 kB
    init almost 2 years ago
  • config.json
    954 Bytes
    init almost 2 years ago
  • configuration_internlm2.py
    7.02 kB
    init almost 2 years ago
  • configuration_internlm_xcomposer2.py
    6.96 kB
    init almost 2 years ago
  • generation_config.json
    176 Bytes
    init almost 2 years ago
  • ixc_utils.py
    1.12 kB
    init almost 2 years ago
  • modeling_internlm2.py
    43.4 kB
    init almost 2 years ago
  • modeling_internlm_xcomposer2.py
    22.4 kB
    init almost 2 years ago
  • pytorch_model-00001-of-00002.bin
    10 GB
    xet
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  • pytorch_model-00002-of-00002.bin
    7.2 GB
    xet
    init almost 2 years ago
  • pytorch_model.bin.index.json
    89.9 kB
    init almost 2 years ago
  • special_tokens_map.json
    713 Bytes
    init almost 2 years ago
  • tokenization_internlm2.py
    8.81 kB
    init almost 2 years ago
  • tokenizer.model
    1.48 MB
    xet
    init almost 2 years ago
  • tokenizer_config.json
    2.38 kB
    init almost 2 years ago
  • training_args.bin
    5.95 kB
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    init almost 2 years ago