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deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B @ ad9f0ae0864d7fbcd1cd905e3c6c5b069cc8b562 supervised-fine-tuned for one pass over the
6400 pre-registered sft_train rows of
cmpatino/direct-opd-sft-deepmath-pilot-data @ 22625ae5db434947195bf862c429cd94504a4809.| steps / global batch | 100 x 64 = 6400 examples (6400 rows available) |
| optimizer | AdamW lr 5e-06, betas (0.9, 0.95), eps 1e-08, wd 0.1 (no decay on 1-D params) |
| schedule | linear decay, 10 warmup steps, grad-norm clip 1.0 |
| precision | fp32 master weights, autocast bf16 compute, bf16 checkpoints |
| max sequence | 3072 tokens (prompt <= 1024, target + EOS <= 2048) |
| supervised tokens | 10,950,054 |
| seed | 42 (data order numpy.random.default_rng(42).permutation(n_train) [PCG64; NOT RandomState/MT19937], n_train=6400) |
| attention | flash_attention_2 (requested auto) |
| target construction | completion-style: prompt via add_generation_prompt=True, target = adapted_target + EOS, prompt tokens masked with -100 |
checkpoint-20/ — optimizer step 20checkpoint-40/ — optimizer step 40checkpoint-60/ — optimizer step 60checkpoint-80/ — optimizer step 80checkpoint-100/ — optimizer step 100checkpoint-100 (so from_pretrained works directly)logs/run_manifest.json, logs/metrics.jsonl — full config, per-step metrics, tokenization
statistics and every post-training assertion resultlogs/run_manifest.json for the full record)teacher_eval split: step 0 0.5155 -> step 20 0.4633 -> step 40 0.4469 -> step 60 0.4428 -> step 80 0.4413 -> step 100 0.4409