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dinesh-bk
/
NepGPT2

Text Generation
Transformers
Safetensors
Nepali
nep_gptv1
custom_code
Model card Files Files and versions
xet
Community

Instructions to use dinesh-bk/NepGPT2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use dinesh-bk/NepGPT2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="dinesh-bk/NepGPT2", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("dinesh-bk/NepGPT2", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use dinesh-bk/NepGPT2 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "dinesh-bk/NepGPT2"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "dinesh-bk/NepGPT2",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/dinesh-bk/NepGPT2
  • SGLang

    How to use dinesh-bk/NepGPT2 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 "dinesh-bk/NepGPT2" \
        --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": "dinesh-bk/NepGPT2",
    		"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 "dinesh-bk/NepGPT2" \
            --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": "dinesh-bk/NepGPT2",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use dinesh-bk/NepGPT2 with Docker Model Runner:

    docker model run hf.co/dinesh-bk/NepGPT2
NepGPT2
581 MB
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  • 1 contributor
History: 7 commits
dinesh-bk's picture
dinesh-bk
Initial commit
3f6ea01 verified about 1 year ago
  • .gitattributes
    1.52 kB
    initial commit about 1 year ago
  • README.md
    5.27 kB
    Update README.md about 1 year ago
  • __init__.py
    162 Bytes
    Create __init__.py about 1 year ago
  • config.json
    450 Bytes
    Initial commit about 1 year ago
  • configuration_nepali_gpt.py
    1.89 kB
    Initial commit about 1 year ago
  • generation_config.json
    74 Bytes
    Initial commit about 1 year ago
  • model.safetensors
    576 MB
    xet
    Initial commit about 1 year ago
  • modeling_nepali_gpt.py
    3.91 kB
    Initial commit about 1 year ago
  • special_tokens_map.json
    692 Bytes
    Initial commit about 1 year ago
  • tokenizer.json
    4.95 MB
    Initial commit about 1 year ago
  • tokenizer_config.json
    251 kB
    Initial commit about 1 year ago
  • training_vs_eval_loss.png
    37.2 kB
    Upload training_vs_eval_loss.png about 1 year ago