AgGPT13nano

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AgGPT-13 nano

New. Nano. Nimble.

BETA

AgGPT-13 nano is the lightweight beta release of the AgGPT-13 model — built to handle everything from quick, simple queries to more complex reasoning and problem-solving.
Powered by Gemma-2 and trained on high-quality datasets (including an inner world model) using the AG artificial generative world model architecture, it delivers capable performance in a compact package.

This version is quantized to INT8 for speed and efficiency, then dequantized on load for use — making it nimble without sacrificing capability.

Features

• Lightweight – Optimized for lower memory usage with INT8 quantization.
• Fast startup – Loads and dequantizes directly into a usable PyTorch model.
• Flexible – Works on CPU or GPU.
• Interactive – Simple ask() method for quick prompting.
• Based on Gemma-2 – Benefits from state-of-the-art NLP and ML research.

Installation & Usage

pip install torch transformers safetensors

Example:

from aggpt13 import AgGPT

agent = AgGPT(model_path="aggpt13/")
response = agent.ask("Hey, who are you?")
print(response)

How It Works

• Loads tokenizer and model config from transformers.
• Reads quantized weights (.safetensors) and quantization parameters (.json).
• Dequantizes weights into float32 and manually loads them into the model.
• Runs entirely in PyTorch, supporting both CPU and CUDA.

License

This project is distributed under the MIT License. For details, see the LICENSE file.