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swayamsingal
/
NanoQuant

Text Generation
PyTorch
Safetensors
GGUF
English
gpt2
Quantized
Pruned
Small
Nano
SBC
Model card Files Files and versions
xet
Community

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

  • Libraries
  • llama-cpp-python

    How to use swayamsingal/NanoQuant with llama-cpp-python:

    # !pip install llama-cpp-python
    
    from llama_cpp import Llama
    
    llm = Llama.from_pretrained(
    	repo_id="swayamsingal/NanoQuant",
    	filename="NanoQuant.gguf",
    )
    
    output = llm(
    	"Once upon a time,",
    	max_tokens=512,
    	echo=True
    )
    print(output)
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • llama.cpp

    How to use swayamsingal/NanoQuant with llama.cpp:

    Install from brew
    brew install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf swayamsingal/NanoQuant
    # Run inference directly in the terminal:
    llama-cli -hf swayamsingal/NanoQuant
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf swayamsingal/NanoQuant
    # Run inference directly in the terminal:
    llama-cli -hf swayamsingal/NanoQuant
    Use pre-built binary
    # Download pre-built binary from:
    # https://github.com/ggerganov/llama.cpp/releases
    # Start a local OpenAI-compatible server with a web UI:
    ./llama-server -hf swayamsingal/NanoQuant
    # Run inference directly in the terminal:
    ./llama-cli -hf swayamsingal/NanoQuant
    Build from source code
    git clone https://github.com/ggerganov/llama.cpp.git
    cd llama.cpp
    cmake -B build
    cmake --build build -j --target llama-server llama-cli
    # Start a local OpenAI-compatible server with a web UI:
    ./build/bin/llama-server -hf swayamsingal/NanoQuant
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf swayamsingal/NanoQuant
    Use Docker
    docker model run hf.co/swayamsingal/NanoQuant
  • LM Studio
  • Jan
  • vLLM

    How to use swayamsingal/NanoQuant with vLLM:

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

    How to use swayamsingal/NanoQuant with Ollama:

    ollama run hf.co/swayamsingal/NanoQuant
  • Unsloth Studio new

    How to use swayamsingal/NanoQuant with Unsloth Studio:

    Install Unsloth Studio (macOS, Linux, WSL)
    curl -fsSL https://unsloth.ai/install.sh | sh
    # Run unsloth studio
    unsloth studio -H 0.0.0.0 -p 8888
    # Then open http://localhost:8888 in your browser
    # Search for swayamsingal/NanoQuant to start chatting
    Install Unsloth Studio (Windows)
    irm https://unsloth.ai/install.ps1 | iex
    # Run unsloth studio
    unsloth studio -H 0.0.0.0 -p 8888
    # Then open http://localhost:8888 in your browser
    # Search for swayamsingal/NanoQuant to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for swayamsingal/NanoQuant to start chatting
  • Docker Model Runner

    How to use swayamsingal/NanoQuant with Docker Model Runner:

    docker model run hf.co/swayamsingal/NanoQuant
  • Lemonade

    How to use swayamsingal/NanoQuant with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull swayamsingal/NanoQuant
    Run and chat with the model
    lemonade run user.NanoQuant-{{QUANT_TAG}}
    List all available models
    lemonade list

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  • .gitattributes
    1.57 kB
    added gguf about 1 year ago
  • NanoQuant.gguf
    1.77 MB
    xet
    added gguf about 1 year ago
  • README.md
    6.2 kB
    _ about 1 year ago
  • adapter_config.json
    869 Bytes
    updates about 1 year ago
  • adapter_model.safetensors
    593 kB
    xet
    updates about 1 year ago
  • comparative_performance_metrics.json
    1.08 kB
    evaluation_Results about 1 year ago
  • config.json
    885 Bytes
    updates about 1 year ago
  • evaluation_results.json
    4.98 kB
    evaluation_Results about 1 year ago
  • generation_config.json
    124 Bytes
    Models after optimisation about 1 year ago
  • merges.txt
    456 kB
    Models after optimisation about 1 year ago
  • pytorch_model.bin

    Detected Pickle imports (3)

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

    What is a pickle import?

    250 MB
    xet
    updates about 1 year ago
  • special_tokens_map.json
    583 Bytes
    Models after optimisation about 1 year ago
  • tokenizer.json
    3.56 MB
    Models after optimisation about 1 year ago
  • tokenizer_config.json
    615 Bytes
    Models after optimisation about 1 year ago
  • vocab.json
    798 kB
    Models after optimisation about 1 year ago