release: shakespeare-nanogpt-3 (checkpoint + ONNX + tokenizer + model card)

README.md

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+---
+license: mit
+language:
+  - en
+library_name: nanogpt
+pipeline_tag: text-generation
+tags:
+  - shakespeare
+  - nanogpt
+  - bpe
+  - gpt
+  - rope
+  - rmsnorm
+  - educational
+datasets:
+  - shakespeare-complete-works
+---
+
+# Model Card — `shakespeare-nanogpt-3` (v3)
+
+> A [sup computer](https://supcpu.romellogoodman.com) release — a small language model studio. [Model page](https://supcpu.romellogoodman.com/models/shakespeare-nanogpt-3/) · [monorepo](https://github.com/romellogoodman/sup-computer) (frozen code: [`projects/shakespeare/models/shakespeare-nanogpt-3/`](https://github.com/romellogoodman/sup-computer/tree/main/projects/shakespeare/models/shakespeare-nanogpt-3), tag `shakespeare-nanogpt-3`) · runs in your browser at [supcpu.romellogoodman.com/model-player](https://supcpu.romellogoodman.com/model-player/).
+
+
+<div class="takeaways">
+<p class="takeaways-label">Key takeaways</p>
+<ul>
+<li>The new series best: an enlarged early-modern-drama corpus + a <strong>1024-token, corpus-trained byte-level BPE</strong> + <strong>float32</strong> training, reaching held-out <code>BPC 1.831</code> at just <strong>11.02M params</strong>.</li>
+<li>The headline is <strong>parameter efficiency</strong>: it beats the prior champion v2 (1.919 at 29.9M) and matches-or-beats a fresh float32 GPT-2-vocab control (1.843 at 29.9M) at <strong>~1/3 the parameters</strong>.</li>
+<li>The BPC edge over that fresh control is only −0.012 — <strong>within single-seed noise</strong>. The clean wins are the params efficiency and beating the prior champion; a <strong>multi-seed</strong> run is the stated next step.</li>
+</ul>
+</div>
+
+The current best model in the [`shakespeare-nanogpt`](https://github.com/romellogoodman/sup-computer/blob/main/projects/shakespeare/README.md)
+series. Where [v2](https://github.com/romellogoodman/sup-computer/blob/main/research-docs/model-cards/shakespeare-nanogpt-2.md) established the modern architecture
+(RoPE, RMSNorm, bias-free) on the Complete Works with GPT-2 BPE, **v3 changes the
+data and the tokenizer, not the architecture**: it trains a small vocabulary *on
+the corpus itself*, enlarges that corpus with contemporary early-modern drama, and
+trains in float32. The result is equal-or-better quality at about a third of the
+size — this remains **LLM-assisted research** (Claude as the *researcher* under
+human direction), not recursive self-improvement.
+
+> **Series note.** Successor to [`shakespeare-nanogpt-2`](https://github.com/romellogoodman/sup-computer/blob/main/research-docs/model-cards/shakespeare-nanogpt-2.md).
+> All versions and the full story are in [`MODELS.md`](https://github.com/romellogoodman/sup-computer/blob/main/projects/shakespeare/MODELS.md);
+> the scoreboard is [`leaderboard.md`](https://github.com/romellogoodman/sup-computer/blob/main/projects/shakespeare/leaderboard.md).
+
+## Model details
+
+| | |
+|---|---|
+| **Version / git tag** | `shakespeare-nanogpt-3` |
+| **Origin** | LLM-assisted research, round 6 float32 re-run (`projects/shakespeare/runs/r6-fp32-bpe1k`) |
+| **Architecture** | modern — RoPE, RMSNorm, bias-free (`core/nanogpt_core/model.py`, vendored into the frozen folder) |
+| **Size** | ~11.02M params (6 layers · 6 heads · 384 embd · 256 context) |
+| **Tokenizer** | **1024-vocab byte-level BPE**, trained on the enlarged corpus (committed `tokenizer.json`; the `meta.pkl` seam, ADR-0012) |
+| **Precision** | **float32** (eliminates the MPS float16 large-vocab logit overflow that confounded round 5) |
+| **Checkpoint** | `models/shakespeare-nanogpt-3/ckpt.pt` (weights not committed — rebuild below) |
+| **Built on** | [nanoGPT](https://github.com/karpathy/nanoGPT) by Andrej Karpathy (MIT) |
+| **Developed with** | Claude Fable 5 ([Claude Code](https://claude.com/claude-code)) as researcher, human oversight |
+| **License** | MIT |
+
+## Intended use
+
+A **learning project** and a demonstration of LLM-assisted model development
+(measured honestly, version over version). Given a few characters it continues
+them in gibberish-but-convincingly-styled Early Modern English. v3's samples pick
+up conventions from the wider corpus — speaker labels and the italic `_Name._`
+stage-direction convention of the Marlowe/Webster editions.
+
+**Out of scope:** real use of the text; any presentation of output as genuine
+Shakespeare (or Marlowe, Jonson, etc.) or as fact. No instruction following, no
+safety tuning. This is **mimicry only**.
+
+## Training data
+
+The **enlarged early-modern-drama corpus**: Shakespeare's Complete Works
+(Gutenberg #100, ~5 MB) plus public-domain contemporary drama — Marlowe (*Doctor
+Faustus*, both *Tamburlaine*s, *Edward II*, *The Jew of Malta*), Jonson
+(*Volpone*, *The Alchemist*, *Every Man in His Humour*), Kyd (*The Spanish
+Tragedy*), Webster (*The Duchess of Malfi*, *The White Devil*), and Dekker. Total
+training text ~7.85M characters; the tokenizer is trained on the **training split
+only**.
+
+Crucially, the **held-out test set is unchanged**: the same fixed
+250k-character Shakespeare slice (`projects/shakespeare/test.txt`) every version
+in the series is scored on. It is excluded from training and never duplicated —
+so v3's BPC stays directly comparable to v1 and v2 despite the larger, broader
+training corpus. Enlarging the corpus also **eliminated the overfit** that defined
+v2's rounds: validation loss fell monotonically instead of bottoming early.
+
+## Training procedure
+
+Trained with the vendored `train.py` on Apple Silicon (MPS), `dtype=float32`,
+`lr=1e-3`, `block_size=256`, 2000 iterations, best-val checkpoint. Float32 is the
+load-bearing change: round 5 hit a float16 instability on MPS (the CUDA-only
+`GradScaler` is disabled there, so large-vocab logits overflowed and forced the
+larger vocabularies to a crippled learning rate). Re-running every vocabulary at
+float32 removed the overflow and let `bpe1k` vs the GPT-2 control finally be
+compared apples-to-apples.
+
+## Evaluation
+
+Scored on the fixed held-out test in **bits-per-character (BPC)** — a
+tokenizer-agnostic metric (total NLL of the test text ÷ its character count ÷
+ln 2), so char-level, GPT-2-BPE, and custom-BPE models are all directly
+comparable. Lower is better.
+
+| Model | Tokenizer | Params | Test BPC |
+|-------|-----------|-------:|---------:|
+| `shakespeare-nanogpt-1` (v1) | char (65) | 10.66M | 2.395 |
+| `shakespeare-nanogpt-2` (v2) | GPT-2 BPE (50257) | 29.94M | 1.919 |
+| r6 float32 GPT-2 control | GPT-2 BPE (50257) | 29.94M | 1.843 |
+| **`shakespeare-nanogpt-3` (v3)** | **BPE (1024)** | **11.02M** | **1.831** |
+| r6 float32 `bpe4k` (unreleased) | BPE (4096) | 12.19M | 1.813 |
+
+Two clean results and one honest caveat:
+
+- **Parameter efficiency (clean win).** v3 reaches equal-or-better BPC than the
+  50k-vocab models at **~1/3 the parameters**. Of v2's 29.9M parameters, ~19.3M
+  *was* the GPT-2 embedding table; a 1024-vocab embedding is ~0.4M. That budget
+  buys capability instead of a giant vocabulary the Shakespeare domain never uses.
+- **Beats the prior champion (clean win).** 1.831 < v2's 1.919 (−4.6%). All three
+  float32 models clear 1.919 — partly because float32 helps every vocabulary (the
+  GPT-2 control alone improves 1.919 → 1.843).
+- **The edge over the fresh control is within noise (the caveat).** v3's −0.012
+  over the float32 GPT-2 control (1.831 vs 1.843) is smaller than the series' known
+  single-seed wobble; the `bpe4k` run is even lower (1.813) but likewise unreplicated.
+  Read the *vs-control* and *bpe1k-vs-bpe4k* gaps as **directionally promising, not
+  decisive.**
+
+> **Chart to add (dataviz pipeline).** A grouped horizontal bar chart, *held-out
+> BPC vs. parameter count* for the five rows above, would make the efficiency
+> headline visible at a glance — v3 and the unreleased `bpe4k` sitting lowest on
+> BPC while also furthest left on params, the two 29.9M GPT-2-vocab models to
+> their right. Per repo convention this must be generated by
+> [`dataviz/`](https://github.com/romellogoodman/sup-computer/blob/main/tools/dataviz/README.md) (add it to `build.py`), not
+> hand-authored; it is described here rather than embedded because that build step
+> is deferred.
+
+## Limitations
+
+- **Single-seed measurements.** Every score is one training run at a fixed seed
+  (`1337`), no variance estimate. v3's win over the *fresh float32 control* and its
+  gap to the unreleased `bpe4k` are within plausible run-to-run noise. The clear
+  results (params efficiency; beating the prior champion) do not depend on that
+  narrow margin. **Multi-seed replication is the explicit next step** — it is what
+  would turn "bpe1k is tied-best" into "bpe1k is best."
+- **`bpe1k` was still improving.** In round 5 its validation loss had not
+  plateaued at 2000 iterations; more iterations would likely lower BPC further, so
+  1.831 is an under-trained floor, not a tuned optimum.
+- **Domain-narrow test, broadened train.** The training corpus now includes
+  non-Shakespeare drama, but the test set is still pure Shakespeare — the metric
+  rewards Shakespeare mimicry specifically.
+- **Still mimicry:** fluent early-modern *texture*, but no meaning, knowledge, or
+  factuality; no instruction following or safety tuning.
+
+## How to use
+
+The BPE steps need the Hugging Face `tokenizers` library, provided ad hoc:
+
+```bash
+# self-contained v3 folder (weights are gitignored — rebuild them)
+cd projects/shakespeare/models/shakespeare-nanogpt-3
+uv run --with tokenizers python prepare.py   # downloads the enlarged corpus, encodes here
+uv run python train.py                        # -> ./ckpt.pt (zero-arg run reproduces v3)
+uv run --with tokenizers python eval.py       # score on the shared held-out test (expect BPC ~1.831)
+uv run --with tokenizers python sample.py --start="ROMEO:"
+```
+
+The 1024-token `tokenizer.json` is committed and **never retrained** — `prepare.py`
+only re-encodes with it, pinning the exact vocabulary.
+
+## Citation / credits
+
+- nanoGPT by Andrej Karpathy (MIT) — model + training code.
+- The Complete Works of Shakespeare and the contemporary early-modern drama
+  (Marlowe, Jonson, Kyd, Webster, Dekker) — all public domain (Project Gutenberg).
+- LLM-assisted research run with Claude Fable 5 (Claude Code) as researcher, human oversight.

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+      ],
+      [
+        "Ġp",
+        "ur"
+      ],
+      [
+        "Ġg",
+        "l"
+      ],
+      [
+        "Ġf",
+        "oll"
+      ],
+      [
+        "Q",
+        "U"
+      ],
+      [
+        "Ġthou",
+        "ght"
+      ],
+      [
+        "W",
+        "e"
+      ],
+      [
+        "un",
+        "e"
+      ],
+      [
+        "Ġb",
+        "est"
+      ],
+      [
+        "m",
+        "e"
+      ],
+      [
+        "ĠM",
+        "y"
+      ],
+      [
+        "Th",
+        "ou"
+      ],
+      [
+        "on",
+        "t"
+      ],
+      [
+        "p",
+        "le"
+      ],
+      [
+        "Ġha",
+        "s"
+      ],
+      [
+        "Ġre",
+        "m"
+      ],
+      [
+        "at",
+        "ter"
+      ],
+      [
+        "ĠI",
+        "f"
+      ],
+      [
+        "ow",
+        "s"
+      ],
+      [
+        "in",
+        "t"
+      ],
+      [
+        "I",
+        "M"
+      ],
+      [
+        "Ġb",
+        "ra"
+      ],
+      [
+        "Ġf",
+        "ool"
+      ],
+      [
+        "Ġke",
+        "ep"
+      ],
+      [
+        "E",
+        "M"
+      ],
+      [
+        "Ġs",
+        "m"
+      ],
+      [
+        "Ġman",
+        "y"
+      ],
+      [
+        "Ġth",
+        "ose"
+      ],
+      [
+        "Ġm",
+        "aster"
+      ],
+      [
+        "Ġpo",
+        "or"
+      ],
+      [
+        "o",
+        "ld"
+      ],
+      [
+        "en",
+        "s"
+      ],
+      [
+        "ak",
+        "es"
+      ],
+      [
+        "b",
+        "e"
+      ],
+      [
+        "a",
+        "id"
+      ],
+      [
+        "i",
+        "ed"
+      ],
+      [
+        "m",
+        "p"
+      ],
+      [
+        "Ġ",
+        "âĢľ"
+      ],
+      [
+        "Ġde",
+        "ad"
+      ],
+      [
+        "re",
+        "t"
+      ],
+      [
+        "O",
+        "S"
+      ],
+      [
+        "c",
+        "ome"
+      ],
+      [
+        "in",
+        "s"
+      ],
+      [
+        "Ġcom",
+        "es"
+      ],
+      [
+        "hi",
+        "p"
+      ],
+      [
+        "Ġno",
+        "ble"
+      ],
+      [
+        "B",
+        "y"
+      ]
+    ]
+  }
+}