jspaulsen/halluci-mate-v1a
jspaulsen/halluci-mate-v1a is an alpha release 0.8 billion parameter chess LLM, based on the Qwen3 architecture, trained from scratch on the Lichess dataset. It utilizes a custom UCI move tokenizer and a 32,768 token context length. This model is specifically designed for generating chess moves, with a focus on constrained decoding to ensure legal move outputs.
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What is halluci-mate-v1a?
halluci-mate-v1a is an alpha release language model specifically designed for chess. Developed by jspaulsen, it is built upon the Qwen3 architecture with approximately 0.6 billion parameters and a substantial 32,768 token context window. Unlike general-purpose LLMs, this model is trained exclusively on the Lichess dataset, focusing on generating chess moves in UCI format.
Key Capabilities & Features
- Chess-specific Training: Trained from scratch on human games from the Lichess database, reflecting human play patterns.
- Custom UCI Tokenizer: Employs a unique tokenizer for UCI moves, including special tokens for game conditions (
<WHITE>,<BLACK>,<DRAW>). - Constrained Decoding: Supports masking logits to legal moves, preventing the generation of illegal chess moves, which is recommended for practical use.
- Qwen3 Architecture: Utilizes a Qwen3-0.6B base with 28 layers and 16 attention heads.
Limitations & Considerations
- Alpha Quality: The model is in an early development stage; move strength and strategic robustness are unvalidated.
- Illegal Move Potential: Without constrained decoding, the raw model may occasionally produce illegal move tokens.
- Human Game Bias: Reflects idiosyncrasies and blunders present in human games, especially at lower ratings.
- FEN Support: Limited support for analyzing positions from arbitrary FENs.