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aifeifei798
/
Mamba3-MIMO-Tiny-HF

Text Generation
Transformers
Safetensors
PyTorch
mamba
mamba3
ssm
mimo
linear-attention
tilelang
Model card Files Files and versions
xet
Community

Instructions to use aifeifei798/Mamba3-MIMO-Tiny-HF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use aifeifei798/Mamba3-MIMO-Tiny-HF with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="aifeifei798/Mamba3-MIMO-Tiny-HF")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("aifeifei798/Mamba3-MIMO-Tiny-HF", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use aifeifei798/Mamba3-MIMO-Tiny-HF with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "aifeifei798/Mamba3-MIMO-Tiny-HF"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "aifeifei798/Mamba3-MIMO-Tiny-HF",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/aifeifei798/Mamba3-MIMO-Tiny-HF
  • SGLang

    How to use aifeifei798/Mamba3-MIMO-Tiny-HF 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 "aifeifei798/Mamba3-MIMO-Tiny-HF" \
        --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": "aifeifei798/Mamba3-MIMO-Tiny-HF",
    		"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 "aifeifei798/Mamba3-MIMO-Tiny-HF" \
            --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": "aifeifei798/Mamba3-MIMO-Tiny-HF",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use aifeifei798/Mamba3-MIMO-Tiny-HF with Docker Model Runner:

    docker model run hf.co/aifeifei798/Mamba3-MIMO-Tiny-HF
Mamba3-MIMO-Tiny-HF
166 MB
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  • 1 contributor
History: 13 commits
aifeifei798's picture
aifeifei798
Update README.md
8195f0d verified 2 months ago
  • mamba3_hf_ready
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  • my_mamba3_tiny
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  • .gitattributes
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  • 1.train_mamba3.py
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  • 2.inference_mamba3_native.py
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  • 3.finalize_to_safetensors.py
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  • 4.hf_load_and_run.py
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  • Mamba3-MIMO-Tiny-HF.webp
    600 kB
    xet
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  • README.md
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  • install.sh
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  • pyproject.toml
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  • requirements.txt
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