Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing

  • Log In
  • Sign Up

FinancialSupport
/
NanoGPT

Text Generation
Transformers
PyTorch
gpt2
text-generation-inference
Model card Files Files and versions
xet
Community
2

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

  • Libraries
  • Transformers

    How to use FinancialSupport/NanoGPT with Transformers:

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

    How to use FinancialSupport/NanoGPT with vLLM:

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

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

    How to use FinancialSupport/NanoGPT with Docker Model Runner:

    docker model run hf.co/FinancialSupport/NanoGPT
NanoGPT
998 MB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 20 commits
FinancialSupport's picture
FinancialSupport
fixed typo
4b9098d almost 3 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 3 years ago
  • README.md
    50 Bytes
    Create README.md almost 3 years ago
  • config.json
    156 Bytes
    fixed typo almost 3 years ago
  • generation_config.json
    119 Bytes
    trasposed almost 3 years ago
  • model.bin

    Detected Pickle imports (3)

    • "torch._utils._rebuild_tensor_v2",
    • "collections.OrderedDict",
    • "torch.FloatStorage"

    What is a pickle import?

    498 MB
    xet
    first commit almost 3 years ago
  • pytorch_model.bin

    Detected Pickle imports (3)

    • "torch.FloatStorage",
    • "torch._utils._rebuild_tensor_v2",
    • "collections.OrderedDict"

    What is a pickle import?

    498 MB
    xet
    our weight, testing that the process works almost 3 years ago
  • tokenizer.json
    1.36 MB
    Upload 2 files almost 3 years ago
  • vocab.json
    1.04 MB
    Upload 2 files almost 3 years ago