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OptimalScale
/
gpt2-inst-tuning

Text Generation
Transformers
PyTorch
gpt2
Generated from Trainer
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use OptimalScale/gpt2-inst-tuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use OptimalScale/gpt2-inst-tuning with Transformers:

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

    How to use OptimalScale/gpt2-inst-tuning with vLLM:

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

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

    How to use OptimalScale/gpt2-inst-tuning with Docker Model Runner:

    docker model run hf.co/OptimalScale/gpt2-inst-tuning
gpt2-inst-tuning
265 MB
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  • 2 contributors
History: 2 commits
diao
Upload 13 files
71d904e about 3 years ago
  • .gitattributes
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    initial commit about 3 years ago
  • README.md
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  • all_results.json
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  • config.json
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  • generation_config.json
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  • merges.txt
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  • pytorch_model.bin
    261 MB
    xet
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  • special_tokens_map.json
    99 Bytes
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  • tokenizer.json
    2.11 MB
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  • tokenizer_config.json
    229 Bytes
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  • train_results.json
    194 Bytes
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  • trainer_state.json
    90 kB
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  • training_args.bin
    4.8 kB
    xet
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  • vocab.json
    798 kB
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