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Fal7acy
/
big-brain-lm

Text Generation
Transformers
PyTorch
English
big-brain-lm
feature-extraction
custom_code
Model card Files Files and versions
xet
Community

Instructions to use Fal7acy/big-brain-lm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Fal7acy/big-brain-lm with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Fal7acy/big-brain-lm", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Fal7acy/big-brain-lm", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use Fal7acy/big-brain-lm with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Fal7acy/big-brain-lm"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Fal7acy/big-brain-lm",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Fal7acy/big-brain-lm
  • SGLang

    How to use Fal7acy/big-brain-lm 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 "Fal7acy/big-brain-lm" \
        --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": "Fal7acy/big-brain-lm",
    		"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 "Fal7acy/big-brain-lm" \
            --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": "Fal7acy/big-brain-lm",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Fal7acy/big-brain-lm with Docker Model Runner:

    docker model run hf.co/Fal7acy/big-brain-lm
big-brain-lm
775 MB
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  • 1 contributor
History: 10 commits
Fal7acy's picture
Fal7acy
Update config.json
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  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    117 Bytes
    Create README.md over 2 years ago
  • config.json
    713 Bytes
    Update config.json over 2 years ago
  • language.py
    7.15 kB
    Upload model over 2 years ago
  • language_config.py
    1.53 kB
    Upload model over 2 years ago
  • pytorch_model.bin
    775 MB
    xet
    Upload model over 2 years ago