halluci-mate-v2a

Alpha release. A chess LLM trained from scratch on the Lichess dataset using the Qwen3-0.6B architecture and a custom UCI move tokenizer. Successor to jspaulsen/halluci-mate-v1a, retrained on a larger Rapid + Classical–only sample for higher-quality move signal. Expect rough edges — move quality, strategy, and robustness are still unvalidated beyond basic smoke tests.

Source: https://github.com/jspaulsen/halluci-mate

Model details

• Architecture: Qwen3 (Qwen3ForCausalLM), ~0.6B parameters
  • 28 layers, hidden size 1024, 16 attention heads (8 KV heads), intermediate size 3072
  • bfloat16, tied word embeddings, RoPE θ = 1,000,000
• Vocabulary: 1,974 tokens — 6 special tokens (<PAD>, <UNK>, <EOS>, <WHITE>, <BLACK>, <DRAW>) + ~1,792 geometric UCI moves + 176 promotion moves
• Context: 32,768 tokens
• Checkpoint: runs-v1/stylish-bug-611/checkpoint-9687

Tokenizer

The tokenizer is custom and is not loadable via AutoTokenizer.from_pretrained. It is defined in src/halluci_mate/chess_tokenizer.py in the source repo. Install the package and use ChessTokenizer() directly.

Inputs are conditioned on the side-to-move winning: each game is prefixed with <WHITE> or <BLACK> (or <DRAW>), followed by the sequence of UCI moves.

Usage

import chess
import torch
from transformers import AutoModelForCausalLM

from halluci_mate.chess_tokenizer import ChessTokenizer
from halluci_mate.game.game import Game
from halluci_mate.inference import ChessInferenceEngine

engine = ChessInferenceEngine.from_checkpoint(
    "jspaulsen/halluci-mate-v2a",
    constrained=True,   # mask logits to legal moves
    temperature=0.0,    # greedy
)

game = Game(board=chess.Board(), condition="<WHITE>")
move = engine.predict(game)
print(move.uci())

Constrained decoding masks the logits to the set of legal UCI moves in the current position, which eliminates illegal-move hallucinations at the cost of potentially hiding model weaknesses. Unconstrained sampling (constrained=False) will occasionally produce illegal tokens — this is expected for an alpha.

Training

• Data: 5,000,000 Lichess games, restricted to Rapid + Classical time controls, filtered to Normal termination, SAN parsed to UCI with python-chess
• Model initialized from config (no pretrained weights) via AutoModelForCausalLM.from_config
• 1 epoch, batch size 128 per device across 2 GPUs, 9,687 optimizer steps, bf16 + flash_attention_2
• Training script: scripts/train.py in the source repo

Compared to v1a, v2a uses the Rapid + Classical filter (longer time controls yield higher move quality at a given Elo) over a larger game count. Headline play strength against Stockfish-skill-5 is roughly unchanged from v1a; legality (especially in endgames) is moderately improved. See the source repo for the full eval breakdown.

Limitations

• Alpha quality; move strength has not been benchmarked against a rated engine
• Constrained decoding is recommended for any real use — the raw model may emit illegal move tokens
• Trained on human games, so idiosyncrasies and blunders at lower ratings are reflected in behavior
• No support for analyzing positions from arbitrary FENs beyond what Game constructs

License

MIT. See the source repo for details.