Instructions to use McaTech/Nonet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use McaTech/Nonet with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf McaTech/Nonet:Q8_0 # Run inference directly in the terminal: llama cli -hf McaTech/Nonet:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf McaTech/Nonet:Q8_0 # Run inference directly in the terminal: llama cli -hf McaTech/Nonet:Q8_0
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 McaTech/Nonet:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf McaTech/Nonet:Q8_0
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 McaTech/Nonet:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf McaTech/Nonet:Q8_0
Use Docker
docker model run hf.co/McaTech/Nonet:Q8_0
- LM Studio
- Jan
- vLLM
How to use McaTech/Nonet with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "McaTech/Nonet" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "McaTech/Nonet", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/McaTech/Nonet:Q8_0
- Ollama
How to use McaTech/Nonet with Ollama:
ollama run hf.co/McaTech/Nonet:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use McaTech/Nonet with Docker Model Runner:
docker model run hf.co/McaTech/Nonet:Q8_0
- Lemonade
How to use McaTech/Nonet with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull McaTech/Nonet:Q8_0
Run and chat with the model
lemonade run user.Nonet-Q8_0
List all available models
lemonade list
- Atomic Chat
Download Precompiled_Binaries_Android/jniLibs/x86/libllama_cli.so from McaTech/Nonet: direct link, hf CLI and curl.
- Browser
- Download file 31.5 MB
-
https://huggingface.co/McaTech/Nonet/resolve/main/Precompiled_Binaries_Android/jniLibs/x86/libllama_cli.so
- Command line
-
hf download hf://McaTech/Nonet/Precompiled_Binaries_Android/jniLibs/x86/libllama_cli.so
-
curl -L -o libllama_cli.so https://huggingface.co/McaTech/Nonet/resolve/main/Precompiled_Binaries_Android/jniLibs/x86/libllama_cli.so
31.5 MB
- Xet hash:
- e37cbec6ef0a791b779a98d1d4b920567a94842666b2e99ffe9eaa8e33056738
- Size of remote file:
- 31.5 MB
- SHA256:
- 20ee17daa25f80c6351505fad9f5989fe0b918b20d64766cc70856a386cf3659
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