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Mahmoud22
/
my_autotrain_llm

Text Generation
Transformers
PyTorch
TensorBoard
llama
Trained with AutoTrain
text-generation-inference
Model card Files Files and versions
xet
Metrics Training metrics Community

Instructions to use Mahmoud22/my_autotrain_llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Mahmoud22/my_autotrain_llm with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Mahmoud22/my_autotrain_llm")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("Mahmoud22/my_autotrain_llm")
    model = AutoModelForCausalLM.from_pretrained("Mahmoud22/my_autotrain_llm")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use Mahmoud22/my_autotrain_llm with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Mahmoud22/my_autotrain_llm"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Mahmoud22/my_autotrain_llm",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Mahmoud22/my_autotrain_llm
  • SGLang

    How to use Mahmoud22/my_autotrain_llm 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 "Mahmoud22/my_autotrain_llm" \
        --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": "Mahmoud22/my_autotrain_llm",
    		"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 "Mahmoud22/my_autotrain_llm" \
            --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": "Mahmoud22/my_autotrain_llm",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Mahmoud22/my_autotrain_llm with Docker Model Runner:

    docker model run hf.co/Mahmoud22/my_autotrain_llm
my_autotrain_llm
105 MB
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  • 1 contributor
History: 17 commits
Mahmoud22's picture
Mahmoud22
Delete training_args.bin
2db2e7d over 2 years ago
  • checkpoint-224
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  • runs
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  • .gitattributes
    1.52 kB
    initial commit almost 3 years ago
  • README.md
    120 Bytes
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  • adapter_config.json
    458 Bytes
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  • adapter_model.bin
    133 Bytes
    xet
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  • added_tokens.json
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  • config.json
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  • pytorch_model.bin
    128 Bytes
    xet
    added all checkpoint-224 files over 2 years ago
  • special_tokens_map.json
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  • tokenizer.json
    1.84 MB
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  • tokenizer.model
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  • tokenizer_config.json
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  • training_params.json
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