Creating the project.

README.md

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+# ARIA — Zero-to-Tiny LLM (Python)
+
+**ARIA** is a beginner-friendly, step-by-step course that takes you from **“Hello World”** to training a **tiny decoder-only, character-level LLM** in Python. Each lesson is a single, runnable file with clear docstrings, doctests where helpful, and minimal dependencies.
+
+> **Note:** This repository’s instructional content was **generated with the assistance of an AI language model**.
+
+---
+
+## What you’ll build
+
+- A progression of tiny language models:
+  - Count-based bigram model → NumPy softmax toy → PyTorch bigram NN
+  - Single-head self-attention → Mini Transformer block
+  - A tiny decoder-only model trained on a small corpus (e.g., Tiny Shakespeare)
+
+---
+
+## Who this is for
+
+- Beginners who can run `python script.py` and have written a basic “Hello World”.
+- Learners who want a **clear path** to an LLM without heavy math or large datasets.
+
+---
+
+## Course outline (lessons)
+
+1. Read a Text File (with docstrings)  
+2. Character Frequency Counter  
+3. Train/Val Split  
+4. Char Vocabulary + Encode/Decode  
+5. Uniform Random Text Generator  
+6. Bigram Counts Language Model  
+7. Laplace Smoothing (compare w/ and w/o)  
+8. Temperature & Top-k Sampling  
+9. Perplexity on Validation  
+10. NumPy Softmax + Cross-Entropy (toy)  
+11. PyTorch Tensors 101  
+12. Autograd Mini-Lab (fit *y = 2x + 3*)  
+13. Char Bigram Neural LM (PyTorch)  
+14. Sampling Function (PyTorch)  
+15. Single-Head Self-Attention (causal mask)  
+16. Mini Transformer Block (pre-LN)  
+17. Tiny Decoder-Only Model (1–2 blocks)  
+18. *(Optional)* Save/Load & CLI Interface
+
+Each lesson includes: **Outcome, Files, Dependencies, Directions, Starter Code with docstrings + doctests, Run, What you learned, Troubleshooting, Mini-exercises, Next lesson.**
+
+---
+
+## Requirements
+
+- **Python**: 3.10+  
+- **OS**: Windows/macOS/Linux (UTF-8 locale recommended)  
+- **Dependencies**:  
+  - Stdlib only until Lesson 9  
+  - **NumPy** for Lessons 8–10  
+  - **PyTorch** (CPU is fine) from Lesson 11 onward  
+- **Hardware**: CPU is enough for all lessons; tiny models, short runs
+
+Install common deps (when needed):
+
+```bash
+pip install numpy torch --upgrade
+
+```