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Nonet

Text Generation
GGUF
English
llama.cpp
quantized
int8
offline-ai
local-llm
chatnonet
conversational
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metadata
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

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