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zeroae
/
calliope-snac-4b-base-4k

Text Generation
Transformers
Safetensors
nemotron_h
audio
tts
snac
multilingual
continued-pretraining
pretrain
nemotron-h
hybrid-mamba
calliope
custom_code
Model card Files Files and versions
xet
Community

Instructions to use zeroae/calliope-snac-4b-base-4k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use zeroae/calliope-snac-4b-base-4k with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="zeroae/calliope-snac-4b-base-4k", trust_remote_code=True)
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("zeroae/calliope-snac-4b-base-4k", trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained("zeroae/calliope-snac-4b-base-4k", trust_remote_code=True)
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use zeroae/calliope-snac-4b-base-4k with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "zeroae/calliope-snac-4b-base-4k"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "zeroae/calliope-snac-4b-base-4k",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/zeroae/calliope-snac-4b-base-4k
  • SGLang

    How to use zeroae/calliope-snac-4b-base-4k 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 "zeroae/calliope-snac-4b-base-4k" \
        --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": "zeroae/calliope-snac-4b-base-4k",
    		"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 "zeroae/calliope-snac-4b-base-4k" \
            --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": "zeroae/calliope-snac-4b-base-4k",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use zeroae/calliope-snac-4b-base-4k with Docker Model Runner:

    docker model run hf.co/zeroae/calliope-snac-4b-base-4k
calliope-snac-4b-base-4k
18.3 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 9 commits
psodre's picture
psodre
fix(cache): make HybridMambaAttentionDynamicCache compatible with cuda_kernels_forward
9dba328 verified about 8 hours ago
  • .gitattributes
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  • README.md
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  • __init__.py
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  • augmented.yaml
    275 Bytes
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  • config.json
    1.55 kB
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  • configuration_nemotron_h.py
    12.1 kB
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  • generation_config.json
    149 Bytes
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  • model.safetensors
    18.3 GB
    xet
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  • modeling_nemotron_h.py
    79.4 kB
    fix(cache): make HybridMambaAttentionDynamicCache compatible with cuda_kernels_forward about 8 hours ago
  • modeling_nemotron_h_augmented.py
    15.1 kB
    Upload modeling_nemotron_h_augmented.py with huggingface_hub about 8 hours ago
  • tokenizer.json
    19.7 MB
    xet
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  • tokenizer_config.json
    354 Bytes
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