Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

yuyaokeji
/
yuyao

Text Generation
Transformers
English
Chinese
materials-science
chemistry
property-prediction
new-materials
materials-informatics
MatVerse
Model card Files Files and versions
xet
Community
1

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

  • Libraries
  • Transformers

    How to use yuyaokeji/yuyao with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="yuyaokeji/yuyao")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("yuyaokeji/yuyao", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use yuyaokeji/yuyao with vLLM:

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

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

    How to use yuyaokeji/yuyao with Docker Model Runner:

    docker model run hf.co/yuyaokeji/yuyao
yuyao
101 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 33 commits
yuyaokeji's picture
yuyaokeji
Update README.md
971be93 verified about 13 hours ago
  • .gitattributes
    1.79 kB
    Upload 10月23ζ—₯.mp4 about 14 hours ago
  • 10月23ζ—₯.mp4
    68.3 MB
    xet
    Upload 10月23ζ—₯.mp4 about 14 hours ago
  • 1155b8926e7098783ccf7849e7855385.mp4
    18.1 MB
    xet
    introduction about 14 hours ago
  • 2026-07-15-18-20-26-1536x1157.jpg
    566 kB
    xet
    Upload 2026-07-15-18-20-26-1536x1157.jpg about 14 hours ago
  • 41c910da467d2dd7640fd69c0f55dbd4.mp4
    14.2 MB
    xet
    Upload 41c910da467d2dd7640fd69c0f55dbd4.mp4 about 14 hours ago
  • README.md
    2.73 kB
    Update README.md about 13 hours ago