If you’re deploying a small model and choosing between GPT-2 and OPT-125M, here’s something that might help your decision that isn’t about benchmarks.
I’ve been measuring internal trajectory stability during inference not output quality, but how the model navigates its own probability space layer by layer. The two models have nearly identical skeletons (12 layers, 768 dims) but their internal dynamics are radically different.
GPT-2 (124M):
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Commits early (around layer 8 of 12)
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High probability concentration (top1 ~0.77)
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Low entropy (~1.35)
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Sometimes enters an unstable “full bifurcation” state (~3.4% of observations)
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Taxonomy: 35% stable, 22% hidden turbulence, 24% committed
OPT-125M (125M):
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Maintains uncertainty much longer
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Low top1 (~0.03), high entropy (~10.2)
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Almost never enters bifurcation (0.0%)
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Taxonomy: 51% stable, 24% hidden turbulence, 18% committed
What this means practically:
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If your task needs decisive, confident output (classification, extraction) → GPT-2’s early commitment helps
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If your task needs exploration, creativity, or safety margin → OPT’s sustained uncertainty is better
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If you’re doing fine-tuning, know that GPT-2 will shift its dynamics significantly; OPT is more stable under perturbation
Why this matters beyond benchmarks:
Same skeleton. Same parameter count. Completely different internal behavior. Benchmark scores won’t tell you this. But if you’re deploying in production, knowing whether your model silently enters unstable states matters.
Hope this helps someone choosing between these two.