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nzl-thu
/
LLaDA-Instruct-JustGRPO-Math500

Text Generation
Transformers
Safetensors
llada
feature-extraction
reasoning
math
diffusion-language-model
custom_code
Model card Files Files and versions
xet
Community

Instructions to use nzl-thu/LLaDA-Instruct-JustGRPO-Math500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use nzl-thu/LLaDA-Instruct-JustGRPO-Math500 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="nzl-thu/LLaDA-Instruct-JustGRPO-Math500", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("nzl-thu/LLaDA-Instruct-JustGRPO-Math500", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use nzl-thu/LLaDA-Instruct-JustGRPO-Math500 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "nzl-thu/LLaDA-Instruct-JustGRPO-Math500"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "nzl-thu/LLaDA-Instruct-JustGRPO-Math500",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/nzl-thu/LLaDA-Instruct-JustGRPO-Math500
  • SGLang

    How to use nzl-thu/LLaDA-Instruct-JustGRPO-Math500 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 "nzl-thu/LLaDA-Instruct-JustGRPO-Math500" \
        --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": "nzl-thu/LLaDA-Instruct-JustGRPO-Math500",
    		"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 "nzl-thu/LLaDA-Instruct-JustGRPO-Math500" \
            --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": "nzl-thu/LLaDA-Instruct-JustGRPO-Math500",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use nzl-thu/LLaDA-Instruct-JustGRPO-Math500 with Docker Model Runner:

    docker model run hf.co/nzl-thu/LLaDA-Instruct-JustGRPO-Math500
LLaDA-Instruct-JustGRPO-Math500
32.1 GB
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  • 1 contributor
History: 5 commits
nzl-thu's picture
nzl-thu
Update README.md
3fd7648 verified about 2 months ago
  • .gitattributes
    1.52 kB
    initial commit about 2 months ago
  • README.md
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  • config.json
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  • configuration_llada.py
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  • generation_config.json
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  • model-00001-of-00007.safetensors
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  • model.safetensors.index.json
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  • modeling_llada.py
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  • special_tokens_map.json
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