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halluci-mate-v2d

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v2d: broadened DPO preference set (threshold 300, lost-positions included)

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Files changed (6) hide show
  1. README.md +111 -0
  2. config.json +63 -0
  3. generation_config.json +12 -0
  4. model.safetensors +3 -0
  5. tokenizer_config.json +34 -0
  6. vocab.json +1976 -0
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+ ---
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+ license: mit
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+ base_model: jspaulsen/halluci-mate-v2b
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+ language:
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+ - en
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+ tags:
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+ - chess
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+ - dpo
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+ - uci
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+ - qwen
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+ pipeline_tag: text-generation
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+ ---
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+
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+ # halluci-mate v2d
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+
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+ DPO fine-tune of [`jspaulsen/halluci-mate-v2b`](https://huggingface.co/jspaulsen/halluci-mate-v2b)
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+ on a broadened Stockfish-vs-model preference set. Same Qwen3-0.6B architecture,
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+ same custom ~1,800-token UCI tokenizer. v2d revisits the export filters used for
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+ [v2c](https://huggingface.co/jspaulsen/halluci-mate-v2c) and improves on it across
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+ nearly every vs-Stockfish axis.
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+
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+ Source: https://github.com/jspaulsen/halluci-mate
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+
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+ ## What changed vs v2c
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+
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+ The training recipe and base model are identical to v2c. Only the preference
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+ dataset changed, along three axes of the `export-dpo` filter:
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+
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+ | | v2c | **v2d** |
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+ |---|---|---|
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+ | `--flavor` | `quality` | `both` (adds legality pairs) |
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+ | `--threshold` | 200 cp | **300 cp** (sharper blunder definition) |
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+ | `--require-consequential` | on | **off** (keeps blunders from already-lost positions) |
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+ | `--exclude-repetition` | on | **off** |
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+ | Pairs | 11,491 | **25,717** |
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+
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+ The hypothesis going in: dropping `--require-consequential` should help the
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+ model learn to keep losing positions losing rather than swindle, and the
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+ threshold-300 cut should reduce label noise by only counting genuinely sharp
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+ blunders. The vs-Stockfish results below validate that hypothesis, especially
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+ in endgame and lost-position phases. The legality-pair re-inclusion is an
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+ incidental change in the same direction that we did not isolate separately.
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+
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+ ## Training data
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+
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+ 25,717 preference pairs derived from the same 10,000-game v2b-vs-Stockfish run
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+ used for v2c (skill 5, depth 12), exported via the
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+ [halluci-mate](https://github.com/jspaulsen/halluci-mate) eval harness:
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+
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+ ```
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+ scripts/eval.py export-dpo <run> --flavor both --threshold 300
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+ ```
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+
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+ ## Training recipe
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+
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+ [`scripts/train_dpo.py`](https://github.com/jspaulsen/halluci-mate/blob/main/scripts/train_dpo.py)
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+ via TRL's `DPOTrainer` — same hyperparameters as v2c:
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+
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+ | | |
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+ |---|---|
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+ | Method | DPO (TRL) |
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+ | Base / reference model | `jspaulsen/halluci-mate-v2b` |
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+ | Pairs | 25,717 (98% train / 2% eval) |
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+ | Learning rate | 1e-5 |
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+ | LR schedule | cosine, 5% warmup |
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+ | Beta | 0.1 |
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+ | Epochs | 2 (trained), checkpoint-500 selected (≈1.27 ep) |
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+ | Per-device batch × grad accum × GPUs | 16 × 2 × 2 = effective 64 |
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+ | Optimizer | paged AdamW 8-bit |
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+ | Precision | bf16 + flash-attention-2 |
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+ | Total steps | 788 (full run); shipped checkpoint at step 500 |
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+ | Hardware | RTX PRO 4500 Blackwell + RTX 3090 (DDP) |
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+
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+ Step 500 was selected post-hoc as the first checkpoint to hit the maximum
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+ `eval_rewards/accuracies` on the held-out split. As with v2c, `eval_loss`
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+ keeps falling and `eval_rewards/margins` keeps growing through later steps
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+ while accuracy plateaus — empirically the later checkpoints are behaviorally
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+ worse on vs-Stockfish.
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+
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+ ## Held-out eval at end of training (step 500)
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+
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+ Note: v2d's eval split contains both legality and quality pairs at threshold
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+ 300, so these numbers are not directly comparable to v2c's held-out numbers.
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+
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+ | metric | value |
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+ |---|---|
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+ | eval_loss | 0.6431 |
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+ | eval_rewards/accuracies | 0.8438 |
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+ | eval_rewards/margins | 0.1086 |
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+
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+ ## vs-Stockfish (skill 5, depth 12, alternating colors, `--sf-analyze`, 100 games)
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+
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+ | metric | v2c | **v2d** | Δ |
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+ |---|---|---|---|
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+ | score_rate | 0.085 | 0.090 | +0.5pp (within n=100 noise) |
96
+ | legal_rate | 0.9755 | **0.9826** | +0.71pp |
97
+ | tactical_oversight_rate | 0.1437 | **0.1363** | -0.74pp |
98
+ | blunder_rate (overall) | 0.0616 | **0.0570** | -0.46pp |
99
+ | blunder_rate (consequential) | 0.0483 | 0.0504 | +0.21pp |
100
+ | blunder_rate (lost positions) | 0.0795 | **0.0658** | -1.37pp |
101
+ | blunder_rate (endgame) | 0.0811 | **0.0714** | -0.97pp |
102
+ | CPL p95 (overall) | 253 | **233** | -20cp |
103
+
104
+ score_rate is within the n=100 noise band; per-move quality improvements on
105
+ tactical_oversight, blunder rate (especially endgame and lost positions),
106
+ and CPL tail are above noise and consistent with the broadened, sharper
107
+ preference set.
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+
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+ ## License
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+
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+ MIT, matching the v2b base.
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