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Farseer: A Refined Scaling Law in Large Language Models
- Farseer introduces a refined scaling law for Large Language Models (LLMs) to improve predictive accuracy across scales.
- The traditional scaling gap between small-scale experiments and resource-intensive production systems is addressed by Farseer.
- Farseer's model loss surface, $L(N,D)$, offers better empirical data fit compared to prior laws like Chinchilla's law.
- The new scaling law reduces extrapolation error by 433% compared to existing models.
- Farseer allows reliable evaluation of different training strategies across various scales, facilitating predictions of large-scale performance.
- The methodology of Farseer enables confident extrapolation from small-scale ablation studies to predict performance at larger scales.
- Insights into optimal compute allocation for LLM training are provided by Farseer, reflecting modern training demands.
- Around 1,000 LLMs were trained across different scales and configurations to validate Farseer, utilizing approximately 3 million NVIDIA H100 GPU hours.
- All models, data, results, and logs are open-sourced on GitHub to encourage further research and collaboration.
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