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36kevin
/
SCAU-IGEM-Model

Text Generation
Transformers
Safetensors
qwen3
finetuned
qlora
rag
embedding
reranker
base-model
thinking-model
Model card Files Files and versions
xet
Community

Instructions to use 36kevin/SCAU-IGEM-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use 36kevin/SCAU-IGEM-Model with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="36kevin/SCAU-IGEM-Model")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("36kevin/SCAU-IGEM-Model", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use 36kevin/SCAU-IGEM-Model with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "36kevin/SCAU-IGEM-Model"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "36kevin/SCAU-IGEM-Model",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/36kevin/SCAU-IGEM-Model
  • SGLang

    How to use 36kevin/SCAU-IGEM-Model 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 "36kevin/SCAU-IGEM-Model" \
        --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": "36kevin/SCAU-IGEM-Model",
    		"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 "36kevin/SCAU-IGEM-Model" \
            --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": "36kevin/SCAU-IGEM-Model",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use 36kevin/SCAU-IGEM-Model with Docker Model Runner:

    docker model run hf.co/36kevin/SCAU-IGEM-Model
SCAU-IGEM-Model
90.7 GB
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  • 1 contributor
History: 9 commits
36kevin's picture
36kevin
docs: update README.md
dc3d26e 6 months ago
  • 0.6B
    fix: fully migrate large JSON/tokenizer files to Git LFS 6 months ago
  • 1.7B
    fix: fully migrate large JSON/tokenizer files to Git LFS 6 months ago
  • 14B thinking
    fix: fully migrate large JSON/tokenizer files to Git LFS 6 months ago
  • 14B
    fix: fully migrate large JSON/tokenizer files to Git LFS 6 months ago
  • 4B
    fix: fully migrate large JSON/tokenizer files to Git LFS 6 months ago
  • 8
    fix: fully migrate large JSON/tokenizer files to Git LFS 6 months ago
  • Qwen3-Embedding-0___6B
    fix: fully migrate large JSON/tokenizer files to Git LFS 6 months ago
  • Qwen3-Reranker-0___6B
    fix: fully migrate large JSON/tokenizer files to Git LFS 6 months ago
  • RAG framework
    Upload Qwen3 QLoRA adapter weights 7 months ago
  • .gitattributes
    1.68 kB
    chore: clean .gitattributes, only track large JSONs with LFS 6 months ago
  • README.md
    2.09 kB
    docs: update README.md 6 months ago