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

bash

# 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

bibtex

@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