rzen
9fd56b6063
Controlled study of how the model degrades. Four variants, each = v1 with a single field replaced (dataclasses.replace), so differences are attributable: - small: capacity down (2L/64d, ~0.38M params) - short: training time down (max_iters 20k->2k) - ctx: context window down (256->64) - data: data quantity down (max_train_tokens->1M; new TrainConfig knob + train.py slice) - scripts/compare.py: sample same prompt across all trained configs with a shared seed, print side by side, write reports/compare.md - tests/test_configs.py: enforces one-parameter-at-a-time (only intended fields differ from v1) + small param count (3 tests). Full suite: 29 passing.
14 lines
401 B
Python
14 lines
401 B
Python
"""Degradation experiment: same as v1, but far fewer training steps.
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Isolates *training time*. Only max_iters changes from v1 (20,000 -> 2,000).
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The cosine LR schedule anneals fully within these 2,000 steps, so this is the
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best model achievable in that budget — not v1 halted mid-schedule.
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"""
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from dataclasses import replace
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import v1
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model = v1.model
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train = replace(v1.train, max_iters=2000)
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