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rinna
/
llama-3-youko-8b

Text Generation
Transformers
Safetensors
Japanese
English
llama
llama-3
text-generation-inference
Model card Files Files and versions
xet
Community
1

Instructions to use rinna/llama-3-youko-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use rinna/llama-3-youko-8b with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="rinna/llama-3-youko-8b")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("rinna/llama-3-youko-8b")
    model = AutoModelForCausalLM.from_pretrained("rinna/llama-3-youko-8b")
  • Inference
  • Local Apps Settings
  • vLLM

    How to use rinna/llama-3-youko-8b with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "rinna/llama-3-youko-8b"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "rinna/llama-3-youko-8b",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/rinna/llama-3-youko-8b
  • SGLang

    How to use rinna/llama-3-youko-8b 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 "rinna/llama-3-youko-8b" \
        --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": "rinna/llama-3-youko-8b",
    		"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 "rinna/llama-3-youko-8b" \
            --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": "rinna/llama-3-youko-8b",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use rinna/llama-3-youko-8b with Docker Model Runner:

    docker model run hf.co/rinna/llama-3-youko-8b
llama-3-youko-8b
16.1 GB
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  • 3 contributors
History: 10 commits
keisawada's picture
keisawada
Update README.md
6af890b verified over 1 year ago
  • .gitattributes
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  • LICENSE
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  • README.md
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  • USE_POLICY.md
    4.7 kB
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  • config.json
    649 Bytes
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  • generation_config.json
    121 Bytes
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  • model-00001-of-00004.safetensors
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  • model.safetensors.index.json
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  • rinna.png
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  • special_tokens_map.json
    73 Bytes
    first commit about 2 years ago
  • tokenizer.json
    9.08 MB
    first commit about 2 years ago
  • tokenizer_config.json
    50.6 kB
    first commit about 2 years ago