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sjadhav3
/
halucination_free

Text Generation
Transformers
PyTorch
TensorBoard
gpt2
Generated from Trainer
text-generation-inference
Model card Files Files and versions
xet
Metrics Training metrics Community
1

Instructions to use sjadhav3/halucination_free with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use sjadhav3/halucination_free with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="sjadhav3/halucination_free")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("sjadhav3/halucination_free")
    model = AutoModelForCausalLM.from_pretrained("sjadhav3/halucination_free")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use sjadhav3/halucination_free with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "sjadhav3/halucination_free"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "sjadhav3/halucination_free",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/sjadhav3/halucination_free
  • SGLang

    How to use sjadhav3/halucination_free 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 "sjadhav3/halucination_free" \
        --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": "sjadhav3/halucination_free",
    		"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 "sjadhav3/halucination_free" \
            --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": "sjadhav3/halucination_free",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use sjadhav3/halucination_free with Docker Model Runner:

    docker model run hf.co/sjadhav3/halucination_free
halucination_free / runs
123 kB
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  • 1 contributor
History: 7 commits
sjadhav3's picture
sjadhav3
End of training
bcea02c about 3 years ago
  • Apr08_20-59-11_573a780516ce
    End of training about 3 years ago
  • Apr08_21-02-10_573a780516ce
    End of training about 3 years ago
  • Apr08_21-02-49_573a780516ce
    End of training about 3 years ago
  • Apr08_21-04-17_573a780516ce
    End of training about 3 years ago
  • Apr08_21-05-43_573a780516ce
    End of training about 3 years ago
  • Apr15_16-39-49_83a2f9e06bdc
    End of training about 3 years ago
  • Apr17_18-15-00_76b716720ad9
    End of training about 3 years ago
  • Apr17_18-42-03_76b716720ad9
    End of training about 3 years ago
  • Apr19_12-40-09_0c129bfce948
    End of training about 3 years ago
  • Apr24_11-34-10_9f1cce977355
    End of training about 3 years ago