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RedHatAI
/
granite-3.1-8b-base-FP8-dynamic

Text Generation
Transformers
Safetensors
English
granite
fp8
vllm
compressed-tensors
Model card Files Files and versions
xet
Community

Instructions to use RedHatAI/granite-3.1-8b-base-FP8-dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use RedHatAI/granite-3.1-8b-base-FP8-dynamic with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="RedHatAI/granite-3.1-8b-base-FP8-dynamic")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("RedHatAI/granite-3.1-8b-base-FP8-dynamic")
    model = AutoModelForCausalLM.from_pretrained("RedHatAI/granite-3.1-8b-base-FP8-dynamic")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use RedHatAI/granite-3.1-8b-base-FP8-dynamic with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "RedHatAI/granite-3.1-8b-base-FP8-dynamic"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "RedHatAI/granite-3.1-8b-base-FP8-dynamic",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/RedHatAI/granite-3.1-8b-base-FP8-dynamic
  • SGLang

    How to use RedHatAI/granite-3.1-8b-base-FP8-dynamic 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 "RedHatAI/granite-3.1-8b-base-FP8-dynamic" \
        --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": "RedHatAI/granite-3.1-8b-base-FP8-dynamic",
    		"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 "RedHatAI/granite-3.1-8b-base-FP8-dynamic" \
            --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": "RedHatAI/granite-3.1-8b-base-FP8-dynamic",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use RedHatAI/granite-3.1-8b-base-FP8-dynamic with Docker Model Runner:

    docker model run hf.co/RedHatAI/granite-3.1-8b-base-FP8-dynamic
granite-3.1-8b-base-FP8-dynamic
8.78 GB
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  • 2 contributors
History: 12 commits
Shubhra Pandit
Upload correct model files
7f2c88d over 1 year ago
  • .gitattributes
    1.52 kB
    initial commit over 1 year ago
  • README.md
    10.6 kB
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  • added_tokens.json
    87 Bytes
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  • config.json
    1.92 kB
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  • generation_config.json
    132 Bytes
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  • merges.txt
    442 kB
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  • model-00001-of-00002.safetensors
    4.99 GB
    xet
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  • model-00002-of-00002.safetensors
    3.79 GB
    xet
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  • model.safetensors.index.json
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  • recipe.yaml
    384 Bytes
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  • special_tokens_map.json
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  • tokenizer.json
    3.48 MB
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  • tokenizer_config.json
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  • vocab.json
    777 kB
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