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ibm-granite
/
granite-swash-3b-a600m

Text Generation
Transformers
Safetensors
granitemoe_swa
language
granite-next
Model card Files Files and versions
xet
Community

Instructions to use ibm-granite/granite-swash-3b-a600m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use ibm-granite/granite-swash-3b-a600m with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="ibm-granite/granite-swash-3b-a600m")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-swash-3b-a600m")
    model = AutoModelForCausalLM.from_pretrained("ibm-granite/granite-swash-3b-a600m", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use ibm-granite/granite-swash-3b-a600m with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "ibm-granite/granite-swash-3b-a600m"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "ibm-granite/granite-swash-3b-a600m",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/ibm-granite/granite-swash-3b-a600m
  • SGLang

    How to use ibm-granite/granite-swash-3b-a600m 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 "ibm-granite/granite-swash-3b-a600m" \
        --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": "ibm-granite/granite-swash-3b-a600m",
    		"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 "ibm-granite/granite-swash-3b-a600m" \
            --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": "ibm-granite/granite-swash-3b-a600m",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use ibm-granite/granite-swash-3b-a600m with Docker Model Runner:

    docker model run hf.co/ibm-granite/granite-swash-3b-a600m
granite-swash-3b-a600m
6.05 GB
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  • 2 contributors
History: 13 commits
yousafshah's picture
yousafshah
granite-swash-3b-a600m
568a94f verified 30 days ago
  • .gitattributes
    1.52 kB
    initial commit about 1 month ago
  • README.md
    5.54 kB
    granite-swash-3b-a600m about 1 month ago
  • config.json
    1.71 kB
    Upload folder using huggingface_hub about 1 month ago
  • generation_config.json
    235 Bytes
    Upload folder using huggingface_hub about 1 month ago
  • model.safetensors
    6.04 GB
    xet
    Granite-4.1 Model family about 1 month ago
  • model.sig
    9.69 kB
    granite-swash-3b-a600m 30 days ago
  • special_tokens_map.json
    579 Bytes
    Upload folder using huggingface_hub about 1 month ago
  • tokenizer.json
    7.15 MB
    Upload folder using huggingface_hub about 1 month ago
  • tokenizer_config.json
    17.7 kB
    Upload folder using huggingface_hub about 1 month ago