Prompt

原始 Prompt

Landscape 16:9 log-scaled plot of training loss vs compute, four curves for different model sizes. X-axis "Training compute (FLOPs)" with log ticks "1e20", "1e21", "1e22", "1e23", "1e24". Y-axis "Validation loss (cross-entropy)" with linear decreasing ticks "3.5", "3.0", "2.5", "2.0", "1.5". Four descending curves with ±1σ shaded bands, labels near tails: "70M params" (slate gray), "1B params" (muted navy), "10B params" (dusty teal), "70B params" (soft terracotta). Warm-copper dashed diagonal line labeled "compute-optimal frontier"; open circles at isoflop crossover points. Legend box top-right. Title: "Empirical scaling laws: loss vs training compute". Subtitle: "four model sizes on a fixed data mixture; shaded bands = ±1 std over 3 seeds."

科研图示

经验缩放律曲线

科研图示精选案例，来源 @GPT-Image2-Skill精选 No. 83。

@GPT-Image2-Skill精选

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