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rwl4
/
gpt2-medium-chat

Text Generation
Transformers
PyTorch
English
gpt2
text-generation-inference
Model card Files Files and versions
xet
Community
3

Instructions to use rwl4/gpt2-medium-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use rwl4/gpt2-medium-chat with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="rwl4/gpt2-medium-chat")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("rwl4/gpt2-medium-chat")
    model = AutoModelForCausalLM.from_pretrained("rwl4/gpt2-medium-chat")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use rwl4/gpt2-medium-chat with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "rwl4/gpt2-medium-chat"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "rwl4/gpt2-medium-chat",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/rwl4/gpt2-medium-chat
  • SGLang

    How to use rwl4/gpt2-medium-chat 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 "rwl4/gpt2-medium-chat" \
        --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": "rwl4/gpt2-medium-chat",
    		"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 "rwl4/gpt2-medium-chat" \
            --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": "rwl4/gpt2-medium-chat",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use rwl4/gpt2-medium-chat with Docker Model Runner:

    docker model run hf.co/rwl4/gpt2-medium-chat
gpt2-medium-chat
4.28 GB
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  • 1 contributor
History: 13 commits
rwl4's picture
rwl4
Update README.md
fd4ce1e about 3 years ago
  • .gitattributes
    1.78 kB
    Initial commit. about 3 years ago
  • README.md
    695 Bytes
    Update README.md about 3 years ago
  • added_tokens.json
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  • config.json
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  • generation_config.json
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  • merges.txt
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  • optimizer.pt
    2.84 GB
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    Initial commit. about 3 years ago
  • pytorch_model.bin
    1.44 GB
    xet
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  • rng_state.pth
    14.6 kB
    xet
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  • scaler.pt

    Pickle imports

    • No problematic imports detected

    What is a pickle import?

    557 Bytes
    xet
    Initial commit. about 3 years ago
  • scheduler.pt
    627 Bytes
    xet
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  • special_tokens_map.json
    533 Bytes
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  • tokenizer_config.json
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  • trainer_state.json
    47.7 kB
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  • training_args.bin
    3.58 kB
    xet
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  • vocab.json
    999 kB
    Initial commit. about 3 years ago