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AndrewThompson1233
/
maba-101m

Text Generation
Transformers
PyTorch
Safetensors
English
maba
maba-v1
maba-v1.1
recurrent
gated-deltanet
gdn
gdn-2
linear-attention
linear-recurrence
state-space-model
ssm
gqa
grouped-query-attention
swiglu
rmsnorm
rope
speculative-decoding
mtp
multi-token-prediction
tinystories
100m
nlp
casual-lm
transformer
qwen
minicpm
benchmark
Model card Files Files and versions
xet
Community

Instructions to use AndrewThompson1233/maba-101m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use AndrewThompson1233/maba-101m with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="AndrewThompson1233/maba-101m")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("AndrewThompson1233/maba-101m", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use AndrewThompson1233/maba-101m with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "AndrewThompson1233/maba-101m"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "AndrewThompson1233/maba-101m",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/AndrewThompson1233/maba-101m
  • SGLang

    How to use AndrewThompson1233/maba-101m 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 "AndrewThompson1233/maba-101m" \
        --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": "AndrewThompson1233/maba-101m",
    		"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 "AndrewThompson1233/maba-101m" \
            --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": "AndrewThompson1233/maba-101m",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use AndrewThompson1233/maba-101m with Docker Model Runner:

    docker model run hf.co/AndrewThompson1233/maba-101m
maba-101m
407 MB
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  • 1 contributor
History: 22 commits
AndrewThompson1233's picture
AndrewThompson1233
docs: update image URLs to absolute resolve links and clean documentation
46cb131 verified 2 days ago
  • assets
    assets: fix memory units (MB) in benchmark comparison 2 days ago
  • maba
    Update maba/config.py with real logos and technical benchmarks 3 days ago
  • .gitattributes
    1.52 kB
    initial commit 3 days ago
  • README.md
    5.7 kB
    docs: update image URLs to absolute resolve links and clean documentation 2 days ago
  • benchmark_results.json
    3.22 kB
    release: v1.1 upgrade benchmark_results.json 3 days ago
  • config.json
    831 Bytes
    release: v1.1 upgrade config.json 3 days ago
  • large_scale_results.json
    2.02 kB
    release: v1.1 upgrade large_scale_results.json 3 days ago
  • model.safetensors
    202 MB
    xet
    Upload folder using huggingface_hub 3 days ago
  • pytorch_model.bin
    202 MB
    xet
    Upload folder using huggingface_hub 3 days ago
  • real_benchmark_results.json
    374 Bytes
    Upload folder using huggingface_hub 3 days ago
  • tokenizer.json
    2.27 MB
    Upload folder using huggingface_hub 3 days ago
  • tokenizer_config.json
    225 Bytes
    Upload folder using huggingface_hub 3 days ago
  • training_results.json
    5.14 kB
    Upload folder using huggingface_hub 3 days ago