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

Libraries

How to use McaTech/Nonet with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="McaTech/Nonet",
	filename="ChatNONET-135m-tuned-q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use McaTech/Nonet with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 Studio new

How to use McaTech/Nonet 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 McaTech/Nonet 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 McaTech/Nonet to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for McaTech/Nonet to start chatting

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

NONET

NONET is a family of offline, quantized large language models fine-tuned for question answering with direct, concise answers. Designed for local execution using llama.cpp, NONET is available in multiple sizes and optimized for Android or Python-based environments.

Model Details

Model Description

NONET is intended for lightweight offline use, particularly on local devices like mobile phones or single-board computers. The models have been fine-tuned for direct-answer QA and quantized to int8 (q8_0) using llama.cpp.

Model Name	Base Model	Size
ChatNONET-135m-tuned-q8_0.gguf	Smollm	135M
ChatNONET-300m-tuned-q8_0.gguf	Smollm	300M
ChatNONET-1B-tuned-q8_0.gguf	LLaMA 3.2	1B
ChatNONET-3B-tuned-q8_0.gguf	LLaMA 3.2	3B

Developed by: McaTech (Michael Cobol Agan)
Model type: Causal decoder-only transformer
Languages: English
License: Apache 2.0
Finetuned from:
- Smollm (135M, 300M variants)
- LLaMA 3.2 (1B, 3B variants)

Uses

Direct Use

Offline QA chatbot
Local assistants (no internet required)
Embedded Android or Python apps

Out-of-Scope Use

Long-form text generation
Tasks requiring real-time web access
Creative storytelling or coding tasks

Bias, Risks, and Limitations

NONET may reproduce biases present in its base models or fine-tuning data. Outputs should not be relied upon for sensitive or critical decisions.

Recommendations

Validate important responses
Choose model size based on your device capability
Avoid over-reliance for personal or legal advice

How to Get Started with the Model

For Android Devices

Try the Android app in my Github: Download ChatNONET APK

You can also build llama.cpp your own and run it

# Clone llama.cpp and build it
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make

# Run the model
./llama-cli -m ./ChatNONET-300m-tuned-q8_0.gguf -p "You are ChatNONET AI assistant." -cnv

Training Details

Finetuning Goal: Direct-answer question answering
Precision: FP16 mixed precision
Frameworks: PyTorch, Transformers, Bitsandbytes
Quantization: int8 GGUF (q8_0) via llama.cpp

Evaluation

Evaluated internally on short QA prompts
Capable of direct factual or logical answers
Larger models perform better on reasoning tasks

Technical Specifications

Architecture:
- Smollm (135M, 300M)
- LLaMA 3.2 (1B, 3B)
Format: GGUF
Quantization: q8_0 (int8)
Deployment: Mobile (Android) and desktop via llama.cpp

Citation

@misc{chatnonet2025,
  title={ChatNONET: Offline Quantized Q&A Models},
  author={Michael Cobol Agan},
  year={2025},
  note={\url{https://huggingface.co/McaTech/Nonet}},
}

Contact

Author: Michael Cobol Agan (McaTech)
Facebook: FB Profile

Downloads last month: 390

GGUF

Model size

0.1B params

Architecture

llama

Hardware compatibility

8-bit