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sshleifer
/
t5-base-cnn

Text Generation
Transformers
PyTorch
t5
text-generation-inference
Model card Files Files and versions
xet
Community
1

Instructions to use sshleifer/t5-base-cnn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use sshleifer/t5-base-cnn with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="sshleifer/t5-base-cnn")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelWithLMHead
    
    tokenizer = AutoTokenizer.from_pretrained("sshleifer/t5-base-cnn")
    model = AutoModelWithLMHead.from_pretrained("sshleifer/t5-base-cnn")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use sshleifer/t5-base-cnn with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "sshleifer/t5-base-cnn"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "sshleifer/t5-base-cnn",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/sshleifer/t5-base-cnn
  • SGLang

    How to use sshleifer/t5-base-cnn 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 "sshleifer/t5-base-cnn" \
        --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": "sshleifer/t5-base-cnn",
    		"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 "sshleifer/t5-base-cnn" \
            --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": "sshleifer/t5-base-cnn",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use sshleifer/t5-base-cnn with Docker Model Runner:

    docker model run hf.co/sshleifer/t5-base-cnn
t5-base-cnn
899 MB
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  • 1 contributor
History: 6 commits
patrickvonplaten's picture
patrickvonplaten
allow flax
d23d8b3 almost 5 years ago
  • .gitattributes
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    allow flax almost 5 years ago
  • colin_preds.txt
    3.41 MB
    Update colin_preds.txt almost 6 years ago
  • colin_targets.txt
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  • config.json
    1.2 kB
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  • pytorch_model.bin
    892 MB
    xet
    Update pytorch_model.bin almost 6 years ago