| --- |
| license: mit |
| tags: |
| - chess |
| - mixture-of-experts |
| - transformer |
| - recurrent |
| - policy |
| - pytorch |
| library_name: pytorch |
| --- |
| |
| # Chess MoE (five 99M experts + router) |
|
|
| Jointly trained Switch MoE. Five **99M** squares64 specialists plus a small |
| MLP router on the incumbent `global_hidden`. Compact vocab 1968. |
|
|
| This file is **`latest.pt` at step 550** (2026-09-14 20:15 UTC). |
| Train loss ~2.4342. Val hard CE ~1.2483. |
|
|
| ## Elo |
|
|
| Search-free greedy policy (legal-masked argmax). No book, no Syzygy, no MCTS. |
| Opponent: Stockfish 19 `UCI_LimitStrength` / `UCI_Elo`, movetime=0.05s, ply cap 160. |
| This is **not** FIDE or Lichess Elo. |
|
|
| **Estimate: 2028 UCI_Elo**. |
| |
| | SF UCI_Elo | Score | W-D-L | Ply-cap (counted as draw) | |
| |---|---|---|---| |
| | 2050 | 0.469 (32g) | 10-10-12 | 1 | |
| |
| _logistic from 32 games vs 2050 score=0.469_ |
| |
| ## Experts |
| |
| - `incumbent` |
| - `puzzle` |
| - `endgame` |
| - `opening` |
| - `middlegame` |
| |
| Router: LayerNorm → 736→256→256→5 (~257,221 params). |
| Hard argmax dispatch. Incumbent encode is reused if the gate picks it. |
| |
| ## Files |
| |
| - `latest.pt` — full bundle (router + all five expert state dicts) |
| - `router.pt` — router head only |
| - `experts/<name>.pt` — each 99M specialist |
| - `router_config.json` |
| - `train.log` |
| - `elo_eval.json` (if present) |
| |
| Load with this repo and `MOVE_VOCAB_VERSION=compact`: |
| |
| ```python |
| from chess_inference import load_checkpoint, get_model_move |
| model = load_checkpoint("latest.pt", device="cuda") |
| ``` |
| |
| Not a drop-in replacement for the standalone incumbent / puzzle / endgame / |
| opening / middlegame repos. Those stay as the pre-joint specialists. |
| |
