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Volko76
/
Qwen2.5-0.5B-Instruct-EXL2-4bits

Text Generation
Transformers
chat
autoquant
exl2
Model card Files Files and versions
xet
Community
1

Instructions to use Volko76/Qwen2.5-0.5B-Instruct-EXL2-4bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Volko76/Qwen2.5-0.5B-Instruct-EXL2-4bits with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Volko76/Qwen2.5-0.5B-Instruct-EXL2-4bits")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Volko76/Qwen2.5-0.5B-Instruct-EXL2-4bits", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use Volko76/Qwen2.5-0.5B-Instruct-EXL2-4bits with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Volko76/Qwen2.5-0.5B-Instruct-EXL2-4bits"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Volko76/Qwen2.5-0.5B-Instruct-EXL2-4bits",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Volko76/Qwen2.5-0.5B-Instruct-EXL2-4bits
  • SGLang

    How to use Volko76/Qwen2.5-0.5B-Instruct-EXL2-4bits 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 "Volko76/Qwen2.5-0.5B-Instruct-EXL2-4bits" \
        --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": "Volko76/Qwen2.5-0.5B-Instruct-EXL2-4bits",
    		"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 "Volko76/Qwen2.5-0.5B-Instruct-EXL2-4bits" \
            --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": "Volko76/Qwen2.5-0.5B-Instruct-EXL2-4bits",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Volko76/Qwen2.5-0.5B-Instruct-EXL2-4bits with Docker Model Runner:

    docker model run hf.co/Volko76/Qwen2.5-0.5B-Instruct-EXL2-4bits
Qwen2.5-0.5B-Instruct-EXL2-4bits / base_model
23 MB
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  • 2 contributors
History: 1 commit
Volko76's picture
Volko76
init
a4b8e5b verified over 1 year ago
  • Qwen2.5-0.5B-Instruct
    init over 1 year ago
  • LICENSE
    11.3 kB
    init over 1 year ago
  • README.md
    4.92 kB
    init over 1 year ago
  • config.json
    659 Bytes
    init over 1 year ago
  • generation_config.json
    242 Bytes
    init over 1 year ago
  • merges.txt
    1.67 MB
    init over 1 year ago
  • tokenizer.json
    7.03 MB
    init over 1 year ago
  • tokenizer_config.json
    7.31 kB
    init over 1 year ago
  • vocab.json
    2.78 MB
    init over 1 year ago