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Jtapsa
/
uuno-1B-7B-long

Text Generation
Transformers
Finnish
English
MoE
Long-Context-Expansion
Model card Files Files and versions
xet
Community

Instructions to use Jtapsa/uuno-1B-7B-long with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Jtapsa/uuno-1B-7B-long with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Jtapsa/uuno-1B-7B-long")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Jtapsa/uuno-1B-7B-long", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use Jtapsa/uuno-1B-7B-long with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Jtapsa/uuno-1B-7B-long"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Jtapsa/uuno-1B-7B-long",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Jtapsa/uuno-1B-7B-long
  • SGLang

    How to use Jtapsa/uuno-1B-7B-long 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 "Jtapsa/uuno-1B-7B-long" \
        --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": "Jtapsa/uuno-1B-7B-long",
    		"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 "Jtapsa/uuno-1B-7B-long" \
            --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": "Jtapsa/uuno-1B-7B-long",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Jtapsa/uuno-1B-7B-long with Docker Model Runner:

    docker model run hf.co/Jtapsa/uuno-1B-7B-long
uuno-1B-7B-long
4.49 MB
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  • 1 contributor
History: 7 commits
Jtapsa's picture
Jtapsa
Update README.md
73df206 verified 19 days ago
  • .gitattributes
    1.52 kB
    initial commit 21 days ago
  • README.md
    121 Bytes
    Update README.md 19 days ago
  • __init__.py
    884 Bytes
    Adding model base files 21 days ago
  • configuration_uuno.py
    13.7 kB
    Adds per-layer RoPE scaling to support YaRN on full-attention layers. 21 days ago
  • modeling_uuno.py
    18.7 kB
    Adds per-layer rotary embedding handling to apply YaRN on full-attention layers. 21 days ago
  • special_tokens_map.json
    65 Bytes
    Adding Tokenizer Files 21 days ago
  • tokenizer.json
    4.45 MB
    Adding Tokenizer Files 21 days ago
  • tokenizer_config.json
    367 Bytes
    Adding Tokenizer Files 21 days ago