在代码大模型（Code LLMs）的预训练中，行业内长期存在一种惯性思维，即把所有编程语言的代码都视为同质化的文本数据，主要关注数据总量的堆叠。然而，现代软件开发本质上是多语言混合的，不同语言的语法特性、语料规模和应用场景差异巨大。如果忽略这些差异，笼统地应用通用的 Scaling Laws，往往会导致性能预测偏差和算力浪费。

在代码大模型（Code LLMs）的预训练中，行业内长期存在一种惯性思维，即把所有编程语言的代码都视为同质化的文本数据，主要关注数据总量的堆叠。然而，现代软件开发本质上是多语言混合的，不同语言的语法特性、语料规模和应用场景差异巨大。如果忽略这些差异，笼统地应用通用的 Scaling Laws，往往会导致性能预测偏差和算力浪费。

Distributed computing is the simultaneous use of more than one computer to solve a problem. It is often used for problems that are so big that no individual computer can handle them. This method of ...

AUSTIN, Texas, June 7, 2022 /PRNewswire/ -- Dask, an open-source Python-native parallel computing library, has announced its latest rebrand. Dask's rebranding includes a new visual style throughout ...

Nvidia has been more than a hardware company for a long time. As its GPUs are broadly used to run machine learning workloads, machine learning has become a key priority for Nvidia. In its GTC event ...