chsm8-sft (v2.1) further trained with DPO so that it follows a natural-language style prompt more strongly ("trade queens as early as possible", "push your pawns aggressively up the board"...). Same architecture and usage as chsm8-sft: decoder over factorized move tokens, cross-attention to a frozen google/embeddinggemma-300m encoding of the prompt (all token states as keys) in the top 6 of 12 layers. Code: github.com/nov3o/llmchess (src/dposft.py, src/evalsft.py). Both use the identical loss and hyperparameters (below); they differ in run length, batch size and LR schedule length. Which to pick. model.pt is the main model because it follows style prompts most strongly (66.5% early queen…
Independent publisher
Van
LegumMagister
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Models on Hugging Face19
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