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HarshalH
/
output_dir

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

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

  • Libraries
  • Transformers

    How to use HarshalH/output_dir with Transformers:

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

    How to use HarshalH/output_dir with vLLM:

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

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

    How to use HarshalH/output_dir with Docker Model Runner:

    docker model run hf.co/HarshalH/output_dir
output_dir / runs
68.6 kB
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  • 1 contributor
History: 11 commits
HarshalH's picture
HarshalH
HarshalH/SFT_LoRA
fefcddd verified over 1 year ago
  • Oct01_23-24-18_10f55bcfa5a2
    End of training over 1 year ago
  • Oct01_23-33-03_10f55bcfa5a2
    End of training over 1 year ago
  • Oct02_00-33-26_1bd55d887019
    End of training over 1 year ago
  • Oct02_00-41-28_1bd55d887019
    HarshalH/OnlySFT over 1 year ago
  • Oct02_00-52-07_d41decf1c6dc
    HarshalH/SFT_LoRA over 1 year ago
  • Oct02_03-12-51_320ec6331933
    HarshalH/SFT_LoRA over 1 year ago
  • Oct02_04-05-35_7171534a0784
    HarshalH/SFT_LoRA over 1 year ago
  • Oct06_04-06-09_a307efffef62
    HarshalH/OnlySFT over 1 year ago
  • Oct06_04-12-39_a307efffef62
    HarshalH/SFT_LoRA over 1 year ago
  • Oct06_04-23-21_40898b2f87bd
    HarshalH/OnlySFT over 1 year ago
  • Oct06_04-31-13_40898b2f87bd
    HarshalH/SFT_LoRA over 1 year ago