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lukasedv
/
fi-2

Text Generation
Transformers
Safetensors
phi-msft
conversational
custom_code
Model card Files Files and versions
xet
Community

Instructions to use lukasedv/fi-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use lukasedv/fi-2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="lukasedv/fi-2", trust_remote_code=True)
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("lukasedv/fi-2", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use lukasedv/fi-2 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "lukasedv/fi-2"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "lukasedv/fi-2",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/lukasedv/fi-2
  • SGLang

    How to use lukasedv/fi-2 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 "lukasedv/fi-2" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "lukasedv/fi-2",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    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 "lukasedv/fi-2" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "lukasedv/fi-2",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use lukasedv/fi-2 with Docker Model Runner:

    docker model run hf.co/lukasedv/fi-2
fi-2
5.56 GB
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  • 1 contributor
History: 4 commits
lukasedv's picture
lukasedv
Update README.md
90b79eb verified over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    170 Bytes
    Update README.md over 2 years ago
  • added_tokens.json
    1.13 kB
    Model upload over 2 years ago
  • config.json
    889 Bytes
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  • configuration_phi.py
    2.03 kB
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  • generation_config.json
    69 Bytes
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  • merges.txt
    456 kB
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  • model-00001-of-00002.safetensors
    4.98 GB
    xet
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  • model-00002-of-00002.safetensors
    577 MB
    xet
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  • model.safetensors.index.json
    24.3 kB
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  • modeling_phi.py
    33.7 kB
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  • special_tokens_map.json
    584 Bytes
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  • tokenizer_config.json
    8.15 kB
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  • vocab.json
    999 kB
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