Add pre-registration draft, timeline skeleton and AI-use log

Claude-Session: https://claude.ai/code/session_01S8KN7NuUCm8nXLmy6eDuVv

06-experiments/README.md

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+# 06-experiments
+
+Plain nanoGPT + TinyStories on an M4 Pro (PyTorch MPS), with QK-norm as a single on/off switch
+and every run logged automatically.
+
+| Path | What |
+|---|---|
+| `preregistration.md` | Hypotheses and how each will be tested, written **before** the main runs |
+| `run.py` | Launch a run. Writes `runs/<id>/run.json` + metrics and adds a row to `runs/index.csv` |
+| `configs/` | Run configs (`spike.py` = tech-spike smoke test only) |
+| `nanogpt/` | Vendored nanoGPT; `README-thesis.md` lists every change from upstream |
+| `data/prepare_tinystories.py` | Downloads + tokenises TinyStories, records SHA-256 checksums |
+| `runs/` | One folder per run (`run.json`, `config.json`, `metrics.jsonl` tracked; checkpoints and raw stdout ignored) |
+| `runs/index.csv` | One row per run: status, QK-norm, LR, depth, seed, best val loss, diverged-at, commit |
+
+## Setup (once)
+
+```sh
+cd 06-experiments
+uv venv --python 3.12 .venv && uv pip install --python .venv/bin/python -r requirements.txt
+.venv/bin/python data/prepare_tinystories.py --spike   # 22 MB, ~1 min
+.venv/bin/python data/prepare_tinystories.py           # full dataset, ~2.2 GB download
+```
+
+## Running
+
+```sh
+.venv/bin/python run.py my-run-name configs/spike.py qk_norm=True learning_rate=3e-3 seed=1 \
+    --note "why I'm running this"
+```
+
+**Habit:** commit before running. `run.py` records the commit SHA. If there are uncommitted
+changes, it saves them as `diff.patch` in the run folder, so a result can always be traced back
+to the exact code, but a clean commit is easier to cite.
+
+## What gets logged (`run.json`)
+
+name, note, status (`completed` / `diverged` / `failed` / `interrupted`), start/end time, exact
+command, requested + resolved config (LR, size, iters, batch, seed, qk_norm…), git commit +
+dirty flag, upstream nanoGPT commit, dataset name + SHA-256 of raw files + token counts,
+machine / macOS / Python / torch versions, and a summary (param count, first/last/best loss,
+iteration where it diverged).
+
+## Tech-spike results (28/10/2026)
+
+See `runs/index.csv`. Pipeline check only: tiny model, 300 steps, small data subset.
+
+## Known issues / decisions still open
+
+- **Vocabulary size dominates the parameter count.** With GPT-2's 50k BPE vocab, a 4-layer
+  256-wide model has ~16M params, ~13M of them in the embedding. For a "10–15M param" model the
+  non-embedding count is far smaller than it sounds. `[Your call: report non-embedding params
+  (as Kaplan et al. and Wortsman et al. do), and/or train a smaller tokenizer on TinyStories.]`
+- MPS runs in float32 (nanoGPT only autocasts on CUDA). Fine for this study; note it in methods.
+- nanoGPT's `estimate_mfu` assumes an A100, so the `mfu` number in the logs is meaningless on a Mac.