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SkyworkAIGC
/
SkyCode

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

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

  • Libraries
  • Transformers

    How to use SkyworkAIGC/SkyCode with Transformers:

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

    How to use SkyworkAIGC/SkyCode with vLLM:

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

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

    How to use SkyworkAIGC/SkyCode with Docker Model Runner:

    docker model run hf.co/SkyworkAIGC/SkyCode
SkyCode
5.47 GB
Ctrl+K
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  • 2 contributors
History: 12 commits
SkyWork
Update README.md
082aa85 over 3 years ago
  • .gitattributes
    1.48 kB
    initial commit over 3 years ago
  • README.md
    6.79 kB
    Update README.md over 3 years ago
  • README_en.md
    3.37 kB
    Rename README_SkyCode_en.md to README_en.md over 3 years ago
  • config.json
    783 Bytes
    Upload 4 files over 3 years ago
  • pytorch_model.bin

    Detected Pickle imports (4)

    • "torch.ByteStorage",
    • "torch.HalfStorage",
    • "collections.OrderedDict",
    • "torch._utils._rebuild_tensor_v2"

    What is a pickle import?

    5.47 GB
    xet
    Upload pytorch_model.bin over 3 years ago
  • tokenization_sky.py
    18.7 kB
    Update tokenization_sky.py over 3 years ago
  • tokenizer_config.json
    343 Bytes
    Upload 4 files over 3 years ago
  • vocab.json
    1.07 MB
    Upload 4 files over 3 years ago