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FrAnKu34t23
/
Test

Text Generation
PEFT
TensorBoard
Safetensors
Transformers
lora
Model card Files Files and versions
xet
Metrics Training metrics Community

Instructions to use FrAnKu34t23/Test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • PEFT

    How to use FrAnKu34t23/Test with PEFT:

    from peft import PeftModel
    from transformers import AutoModelForCausalLM
    
    base_model = AutoModelForCausalLM.from_pretrained("distilgpt2")
    model = PeftModel.from_pretrained(base_model, "FrAnKu34t23/Test")
  • Transformers

    How to use FrAnKu34t23/Test with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="FrAnKu34t23/Test")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("FrAnKu34t23/Test", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use FrAnKu34t23/Test with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "FrAnKu34t23/Test"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "FrAnKu34t23/Test",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/FrAnKu34t23/Test
  • SGLang

    How to use FrAnKu34t23/Test 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 "FrAnKu34t23/Test" \
        --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": "FrAnKu34t23/Test",
    		"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 "FrAnKu34t23/Test" \
            --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": "FrAnKu34t23/Test",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use FrAnKu34t23/Test with Docker Model Runner:

    docker model run hf.co/FrAnKu34t23/Test
Test / checkpoint-1050
9.76 MB
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  • 1 contributor
History: 1 commit
FrAnKu34t23's picture
FrAnKu34t23
Upload merged fine-tuned risk prediction model
35cb202 verified about 1 year ago
  • README.md
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  • adapter_config.json
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  • adapter_model.safetensors
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  • merges.txt
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  • optimizer.pt
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  • rng_state.pth
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  • scheduler.pt
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  • special_tokens_map.json
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  • tokenizer.json
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  • tokenizer_config.json
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  • trainer_state.json
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  • training_args.bin
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  • vocab.json
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