Update README.md for clearer documentation

README.md

@@ -10,12 +10,45 @@ language:
 
 ## New. Nano. Nimble.
 
-### BETA 
+### **BETA**
 
-AgGPT-13 nano is a lightweight beta version of the powerful AgGPT-13 model, designed to assist with a wide range of tasks, from simple queries to complex problem-solving. It is built on the latest advancements in natural language processing and machine learning.
+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.
 
-AgGPT-13 nano is based on Gemma-2 and was trained on high quality data including an inner world model, using the AG artificial generative world model architecture.
+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
+
+```bash
+pip install torch transformers safetensors
+```
+
+Example:
+
+```python
+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 licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+This project is distributed under the MIT License. For details, see the [LICENSE](LICENSE) file.
+