Model Card — daydream-chess-nanogpt-1 (v1, Regular)
A
sup computer release — a small language model studio.
Model page ·
monorepo (frozen code:
projects/daydream/models/daydream-chess-nanogpt-1/, tag
daydream-chess-nanogpt-1) · runs in your browser at
www.supcpu.com/model-player.
Key takeaways
- A 2.66M-param char-level GPT that renders illegal chess moves as dim near-misses instead of masking them away — the sampler's resample-until-legal loop is the artwork's mechanism, not a correctness patch on top of it.
- Trained on 15,000 real Lichess games, deliberately mid-rating (~1400–1800 Elo) rather than elite play — too-clean games were rejected as a corpus source because they kill the near-miss texture the dream mechanic needs.
- The release gate is not win rate. It's two automated checks: 100% clean game completion and a 35.3% legal-move rate on the model's raw, unresampled first try — a genuine legality-learning signal, not a strength claim.
- First of three tiers in the daydream family (Micro 5×5, Regular 8×8, Grand 12×10) — the first project in this monorepo with an external non-Python engine dependency, Fairy-Stockfish.
A chess-move GPT that doesn't play chess so much as
hallucinate it. Legal
moves snap into focus; illegal moves render as dim near-misses instead of
being masked or rejection-sampled away — the sampler is the aesthetic
decision, not a correctness filter. First release in the
daydream series, and the standard
8×8 board tier — "Regular," the implicit default among Micro (5×5) and
Grand (12 files × 10 ranks).
Render illegal moves, don't discard them. Most chess-model work treats
illegal output as failure to be masked or rejection-sampled away. Daydream
inverts that: a candidate move is either legal (it snaps into focus and
becomes the game's actual move) or illegal (a rejected dream, kept rather
than thrown away). The sampler's resample-until-legal loop is the artwork's
mechanism, not a bug-fix on top of it.
Model details
| |
|---|
| Version / git tag | daydream-chess-nanogpt-1 (research run regular-r1) |
| Architecture | modern char-level (RoPE, RMSNorm, bias-free) on the shared core engine — no vendored base engine |
| Size | 6 layers · 6 heads · 192 embedding dim · 256 context · dropout 0.1 · ~2.66M params |
| Tokenizer | character-level, 21-char vocabulary over UCI move text (files a–h, ranks 1–8, promotion letters q/r/b/n, space, newline) — meta.pkl is the contract (ADR-0012) |
| Checkpoint | projects/daydream/models/daydream-chess-nanogpt-1/ (weights not committed — regenerates deterministically below) |
| Built on | the monorepo's shared core engine |
| Developed with | Claude (Claude Code) |
| License | MIT |
Intended use
An exhibit exploring what a chess-move model looks like when illegal output
is rendered rather than hidden. Not intended to play strong chess — legality
and interestingness of the near-misses are the point, not playing strength.
Pairs with harness.py, which plays the model against Fairy-Stockfish,
resampling on illegal moves until one lands (or forcing a random legal move
once a resample cap is hit, so games always complete).
Out of scope. Not a chess engine and not evaluated as one — no win-rate
claims are made or should be inferred. Not a general-purpose language model;
its entire vocabulary is UCI chess-move syntax.
Training data
15,000 games from the
Lichess open database
(January 2018 monthly dump), filtered to games where both players are
rated 1400–1800 Elo — deliberately mid-band: strong enough for coherent
positional shape, loose enough to produce the near-miss texture the dream
mechanic needs. Elite/engine games were explicitly rejected as a corpus
source elsewhere in this project's design — too-clean play kills the texture.
Streamed and filtered directly from the compressed dump (zstd decode + Elo
filter in one pass via
python-chess) without ever storing the full ~5.5GB
file. Converted from SAN to UCI move notation. Corpus is downloaded, not
committed (regenerates via
fetch_filtered.py); only derived artifacts
(
*.bin,
*.pkl,
*.pt) are gitignored.
Training procedure
- Optimizer: AdamW, LR 3e-4 with cosine decay to 3e-5, 100 warmup iters, β₂ 0.99, batch size 64.
- Run: 3,000 iterations, best val loss 0.858 (
always_save_checkpoint).
- Hardware: Apple Silicon Mac (MPS / Metal backend),
torch.compile disabled.
Evaluation
Verification runs harness.py: the model plays full games against a
skill-limited Fairy-Stockfish opponent, resampling on illegal moves. The two
automated gate metrics — nothing else is an automated gate, per this
project's design:
| Metric | Result (30 games) |
|---|
| Clean completion rate | 30/30 (100%) — every game reached a natural end (checkmate/stalemate/ply cap) with no pipeline crash |
| Legal-move rate (first try) | 258/731 (35.3%) — over a third of the model's move proposals are legal in the current position on the very first sample, with no resampling |
What 35.3% means here
This is not a chess-strength number — win rate against the opponent is
explicitly not part of this project's release gate. It's a legality-learning
number: roughly one in three raw samples from the model, with no rejection
sampling applied, land on a real legal move in the actual current position.
The other two-thirds are the dream — syntactically valid UCI strings
(e2e4-shaped) that are illegal here, rendered rather than discarded by the
harness's resample loop.
Limitations
- Not evaluated for playing strength, deliberately — this project's gate
is legality and completion, not win rate.
- Legality is learned, not guaranteed. Even the harness's resample loop
has a cap; beyond it, a uniformly random legal move is forced so the game
still completes. That fallback move is not the model's "dream" — it's the
harness's safety valve.
- UCI-only vocabulary. No SAN, no natural language, no commentary — 21
characters, chess moves and nothing else.
- No weights in the tree (ADR-0002).
How to reproduce
1cd projects/daydream/models/daydream-chess-nanogpt-1
2python fetch_filtered.py # -> games.txt (network; Lichess Jan 2018 dump)
3python prepare.py # -> regular/{train,val}.bin + meta.pkl
4python train.py config.py # -> ./ckpt.pt (3000 iters, val ~0.86)
5python harness.py --games 30 # verification: legal-move rate, clean-completion rate
Requires Fairy-Stockfish on
PATH (
brew install fairy-stockfish) for
harness.py — see
ADR-0021.
Citation / credits
- The shared
core engine (modern nanoGPT lineage — RoPE, RMSNorm, bias-free).
- Fairy-Stockfish — legality arbiter and self-play engine for the sibling Micro/Grand tiers.
- The Lichess open database — corpus source.
- Set up and trained with Claude (Claude Code).
