22 lines
1.8 KiB
JSON
22 lines
1.8 KiB
JSON
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{
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"schema_version": 1,
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"measured_at_kst": "2026-07-12T22:38:00+09:00",
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"hardware": "NVIDIA B200 183359 MiB reported total; 178.35 GiB PyTorch-visible capacity",
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"smoke_evidence": {
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"attempted_profile": {"per_device_batch_size": 2, "gradient_accumulation_steps": 8, "gradient_checkpointing": false, "effective_batch_size": 16},
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"outcome": "OOM before completing the 20-step smoke across every launched unified baseline; representative process used 176.87 GiB and failed a 1.62 GiB allocation. KTO also OOMed at the optimizer step.",
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"paper_metric": false
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},
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"selected_full_training_profile": {
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"per_device_batch_size": 2,
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"gradient_accumulation_steps": 8,
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"gradient_checkpointing": false,
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"attention_implementation": "PyTorch SDPA with cuDNN SDPA disabled",
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"effective_batch_size": 16,
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"applies_symmetrically_to_all_unified_pairwise_methods": true,
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"kto_implementation_constraint": "TRL KTO requires actual batch size > 1. KTO uses batch 2 / accumulation 8 and identical AdamW with foreach=False. PyTorch SDPA is used with cuDNN SDPA disabled. The fixed base reference model is placed on a second B200 and its exact logits are returned to the policy device; the policy/optimizer remain on the first B200. This avoids a measured ~6-hour reference-precompute path while leaving the KTO loss, actual/effective batch, optimizer, and 900-step budget unchanged."
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},
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"retry_semantics": "Resource selection is separated from method-stability attempts. Full attempt 1 uses the smoke-validated SDPA batch-2 profile and retains the preregistered method LR/anchor; attempts 2 and 3 fall back to batch 1 / accumulation 16 / gradient checkpointing and apply the declared LR/anchor stabilization recipe.",
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"provenance_note": "No reward or sealed-evaluation result was available when this hardware profile was selected."
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}
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