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WiseIntelligence
/
orca_mini_3b-Optimum-ONNX

Text Generation
Transformers
ONNX
llama
Model card Files Files and versions
xet
Community

Instructions to use WiseIntelligence/orca_mini_3b-Optimum-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use WiseIntelligence/orca_mini_3b-Optimum-ONNX with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="WiseIntelligence/orca_mini_3b-Optimum-ONNX")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("WiseIntelligence/orca_mini_3b-Optimum-ONNX")
    model = AutoModelForCausalLM.from_pretrained("WiseIntelligence/orca_mini_3b-Optimum-ONNX")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use WiseIntelligence/orca_mini_3b-Optimum-ONNX with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "WiseIntelligence/orca_mini_3b-Optimum-ONNX"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "WiseIntelligence/orca_mini_3b-Optimum-ONNX",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/WiseIntelligence/orca_mini_3b-Optimum-ONNX
  • SGLang

    How to use WiseIntelligence/orca_mini_3b-Optimum-ONNX 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 "WiseIntelligence/orca_mini_3b-Optimum-ONNX" \
        --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": "WiseIntelligence/orca_mini_3b-Optimum-ONNX",
    		"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 "WiseIntelligence/orca_mini_3b-Optimum-ONNX" \
            --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": "WiseIntelligence/orca_mini_3b-Optimum-ONNX",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use WiseIntelligence/orca_mini_3b-Optimum-ONNX with Docker Model Runner:

    docker model run hf.co/WiseIntelligence/orca_mini_3b-Optimum-ONNX
orca_mini_3b-Optimum-ONNX
27.4 GB
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  • 1 contributor
History: 11 commits
WiseIntelligence's picture
WiseIntelligence
Upload generation_config.json with huggingface_hub
d00a7ad over 2 years ago
  • .gitattributes
    1.65 kB
    Upload decoder_with_past_model.onnx_data with huggingface_hub over 2 years ago
  • config.json
    677 Bytes
    Upload config.json with huggingface_hub over 2 years ago
  • decoder_model.onnx
    2.47 MB
    xet
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  • decoder_model.onnx_data
    13.7 GB
    xet
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  • decoder_with_past_model.onnx
    2.5 MB
    xet
    Upload decoder_with_past_model.onnx with huggingface_hub over 2 years ago
  • decoder_with_past_model.onnx_data
    13.7 GB
    xet
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  • generation_config.json
    132 Bytes
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  • special_tokens_map.json
    550 Bytes
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  • tokenizer.json
    1.98 MB
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  • tokenizer.model
    534 kB
    xet
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  • tokenizer_config.json
    905 Bytes
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