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yunconglong
/
13B_MATH_DPO

Text Generation
Transformers
Safetensors
mixtral
Mixture of Experts
DPO
RL-TUNED
text-generation-inference
Model card Files Files and versions
xet
Community
1

Instructions to use yunconglong/13B_MATH_DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use yunconglong/13B_MATH_DPO with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="yunconglong/13B_MATH_DPO")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("yunconglong/13B_MATH_DPO")
    model = AutoModelForCausalLM.from_pretrained("yunconglong/13B_MATH_DPO")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use yunconglong/13B_MATH_DPO with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "yunconglong/13B_MATH_DPO"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "yunconglong/13B_MATH_DPO",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/yunconglong/13B_MATH_DPO
  • SGLang

    How to use yunconglong/13B_MATH_DPO 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 "yunconglong/13B_MATH_DPO" \
        --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": "yunconglong/13B_MATH_DPO",
    		"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 "yunconglong/13B_MATH_DPO" \
            --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": "yunconglong/13B_MATH_DPO",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use yunconglong/13B_MATH_DPO with Docker Model Runner:

    docker model run hf.co/yunconglong/13B_MATH_DPO
13B_MATH_DPO
25.8 GB
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  • 1 contributor
History: 4 commits
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yunconglong
Update README.md
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