Creating the project.

prompt.md

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+# Prompt.md
+
+Copy the prompt below exactly to replicate this course:
+
+```You are an expert Python instructor. Generate a complete, beginner-friendly course called
+“ARIA — Zero-to-Tiny LLM (Python)” that takes a learner from “Hello World” to training a tiny
+decoder-only, character-level LLM in ~17–18 single-file lessons. No safety/guardrail features;
+assume a controlled learning environment.
+
+=== Audience & Scope
+- Audience: absolute beginners who have only written “Hello World”.
+- Language: Python.
+- Goal: build up to a tiny decoder-only LLM trained on a small corpus (e.g., Tiny Shakespeare).
+- Keep each lesson runnable in a single .py file (≤ ~200 lines where feasible).
+
+=== Output Format (for EACH lesson)
+Use this exact section order:
+1) Title
+2) Duration (estimate)
+3) Outcome (what they will accomplish)
+4) Files to create (filenames)
+5) Dependencies (Python stdlib / NumPy / PyTorch as specified)
+6) Step-by-step Directions
+7) Starter code (complete, runnable) with:
+   - A clear module docstring that includes: what it does, how to run, and notes.
+   - Function-level Google-style docstrings (Args/Returns/Raises) + at least one doctest where reasonable.
+8) How to run (CLI commands)
+9) What you learned (bullets)
+10) Troubleshooting (common errors + fixes)
+11) Mini-exercises (3–5 quick tasks)
+12) What’s next (name the next lesson)
+
+=== Curriculum (keep these names and order)
+01) Read a Text File (with docstrings)
+02) Character Frequency Counter
+03) Train/Val Split
+04) Char Vocabulary + Encode/Decode
+05) Uniform Random Text Generator
+06) Bigram Counts Language Model
+07) Laplace Smoothing (compare w/ and w/o)
+08) Temperature & Top-k Sampling
+09) 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
+
+=== Constraints & Defaults
+- Dataset: do NOT auto-download. Expect a local `data.txt`. If missing, include a tiny built-in fallback sample so scripts still run.
+- Encoding: UTF-8. Normalize newlines to "\n" for consistency.
+- Seeds: demonstrate reproducibility (`random`, `numpy`, `torch`).
+- Dependencies:
+  * Stdlib only until Lesson 9;
+  * NumPy in Lessons 8–10;
+  * PyTorch from Lesson 11 onward.
+- Training defaults (for Lessons 13+):
+  * Batch size ~32, block size ~128, AdamW(lr=3e-4).
+  * Brief note on early stopping when val loss plateaus.
+- Inference defaults:
+  * Start with greedy; then temperature=0.8, top-k=50.
+- Keep code clean: type hints where helpful; no frameworks beyond NumPy/PyTorch; no external data loaders.
+
+=== Lesson 1 Specifics
+For Lesson 1, include:
+- Module docstring with Usage example (`python 01_read_text.py`).
+- Functions: `load_text(path: Optional[Path])`, `normalize_newlines(text: str)`,
+  `make_preview(text: str, n_chars: int = 200)`, `report_stats(text: str)`, `main()`.
+- At least one doctest per function where reasonable.
+- Fallback text snippet if `data.txt` isn’t found.
+- Output: total chars, unique chars, 200-char preview with literal "\n".
+
+=== Delivery
+- Start with a short “How to use this repo” preface and a file tree suggestion.
+- Then render Lessons 01–18 in order, each with the exact section headings above.
+- End with a short FAQ (Windows vs. macOS paths, UTF-8 issues, CPU vs. GPU notes).
+
+Generate now.
+```