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haoanh98
/
Viet_Captioning

Image-Text-to-Text
Transformers
google-tensorflow TensorFlow
vision-encoder-decoder
generated_from_keras_callback
Model card Files Files and versions
xet
Community

Instructions to use haoanh98/Viet_Captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use haoanh98/Viet_Captioning with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-text-to-text", model="haoanh98/Viet_Captioning")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForImageTextToText
    
    tokenizer = AutoTokenizer.from_pretrained("haoanh98/Viet_Captioning")
    model = AutoModelForImageTextToText.from_pretrained("haoanh98/Viet_Captioning")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use haoanh98/Viet_Captioning with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "haoanh98/Viet_Captioning"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "haoanh98/Viet_Captioning",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/haoanh98/Viet_Captioning
  • SGLang

    How to use haoanh98/Viet_Captioning 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 "haoanh98/Viet_Captioning" \
        --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": "haoanh98/Viet_Captioning",
    		"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 "haoanh98/Viet_Captioning" \
            --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": "haoanh98/Viet_Captioning",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use haoanh98/Viet_Captioning with Docker Model Runner:

    docker model run hf.co/haoanh98/Viet_Captioning
Viet_Captioning
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  • 1 contributor
History: 2 commits
haoanh98's picture
haoanh98
Upload model
94527aa over 3 years ago
  • .gitattributes
    1.38 kB
    initial commit over 3 years ago
  • README.md
    847 Bytes
    Upload model over 3 years ago
  • config.json
    4.65 kB
    Upload model over 3 years ago
  • tf_model.h5
    1,000 MB
    xet
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