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---
license: apache-2.0
language:
- en
library_name: llama.cpp
tags:
- gguf
- quantized
- int8
- offline-ai
- local-llm
- chatnonet
model_type: causal
inference: true
pipeline_tag: text-generation
---
# 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](https://github.com/Mca-Tech/ChatNONET)
### 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](https://www.facebook.com/michael.cobol.agan.2025/)
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