Update README.md

README.md

@@ -1,9 +1,10 @@
 # TinyGPT
-Like [NanoGPT](https://github.com/karpathy/nanoGPT) but tiny. It is also inspired by [Tinygrad](https://github.com/tinygrad/tinygrad), [Pytorch](https://github.com/pytorch/pytorch) and [MLX](https://github.com/ml-explore/mlx).
+TinyGPT is a minimalistic library for implementing, training, and performing inference on GPT models from scratch in Python, with no external dependencies. Inspired by [NanoGPT](https://github.com/karpathy/nanoGPT), [Tinygrad](https://github.com/tinygrad/tinygrad), [Pytorch](https://github.com/pytorch/pytorch), and [MLX](https://github.com/ml-explore/mlx), TinyGPT aims to be as educational as possible, avoiding complex optimizations that might obscure the underlying concepts.
 
-The main objective of this project is to be as didactic as possible, avoiding optimizations that make the code difficult to understand.
-
-I hope we can understand how to train and run a model like GPT-3 using as few libraries as possible and programming everything from scratch, including the library to train and run the model.
+## Features
+- **Pure Python Implementation**: TinyGPT is written entirely in Python with no external dependencies, making it easy to follow and modify.
+- **Didactic Focus**: Prioritizes readability and understanding over optimization, making it an excellent learning tool.
+- **Modular Design**: The library is divided into several modules, each focusing on a specific aspect of training and inference.
 
 ## Installation
 The current recommended way to install TinyGPT is from source.
@@ -16,12 +17,36 @@ $ python -m pip install -e .
 ```
 Don't forget the `.` at the end!
 
-## Usage
-[TO DO]
+## Project Structure
+  - `buffer.py`: Provides a low-level implementation of array operations, similar to NumPy arrays.
+  - `dataset.py`: Handles data loading and preprocessing.
+  - `losses.py`: Contains implementations of loss functions used for training.
+  - `mlops.py`: Provides a low-level implementation of array operations, similar to NumPy arrays.
+  - `module.py`: Defines the base module from which all model components inherit.
+  - `nn.py`: Contains the neural network components, including layers and activation functions.
+  - `optimizers.py`: Implements optimization algorithms used during training.
+  - `tensor.py`: Provides a minimal tensor implementation to support basic tensor operations.
+  - `tokenizer.py`: Handles tokenization of text data for model input.
+  - `utils.py`: Contains miscellaneous utility functions used across the library.
+
+## Examples
+The `examples/` directory contains scripts demonstrating how to use TinyGPT for various tasks:
+
+  - `gpt.py`: A basic example of training and using a GPT model.
+  - `mnist.py`: An example of using TinyGPT's neural network components on the MNIST dataset.
+
+To run these examples, navigate to the examples/ directory and execute the Python scripts:
+
+```bash
+$ cd examples
+$ python gpt.py
+$ python mnist.py
+```
 
+These examples will guide you through setting up the model, training it, and performing inference, providing a hands-on understanding of how TinyGPT works.
 
 ## Documentation
-Documentation along with a quick start guide can be found in the docs/ directory.
+Documentation along with a quick start guide can be found in the `docs/` directory.
 
 ## Contributing
 Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.