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Comparison with Larger ModelsA useful comparison is within the same scaling regime, since training compute, dataset size, and infrastructure scale increase dramatically with each generation of frontier models. The newest models from other labs are trained with significantly larger clusters and budgets. Across a range of previous-generation models that are substantially larger, Sarvam 105B remains competitive. We have now established the effectiveness of our training and data pipelines, and will scale training to significantly larger model sizes.。关于这个话题，新收录的资料提供了深入分析
，这一点在新收录的资料中也有详细论述
We can even go ahead and write a quick time-travel function like the one below to replay any execution trace locally, complete with built-in support for detecting time paradoxes!，详情可参考新收录的资料
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