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.
15 lines
511 B
Python
15 lines
511 B
Python
"""Degradation experiment: same as v1, but a much smaller model.
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Isolates *capacity*. Only the model dimensions change; context length and the
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training schedule match v1 exactly. ~0.38M params vs v1's 4.26M — and only
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~0.1M of those are non-embedding (the 4,096-token embedding table dominates a
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model this small, so the actual 'reasoning' capacity shrinks even harder).
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"""
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from dataclasses import replace
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import v1
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model = replace(v1.model, n_layers=2, d_model=64, n_heads=2, d_ff=256)
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train = v1.train
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