The scaling of large language models has greatly improved natural language understanding, generation, and reasoning. In this work, we develop a system that trained a trillion-parameter language model on a cluster of Ascend 910 AI processors and MindSpore framework, and present the language model with 1.085T parameters named PanGu-Σ. With parameter inherent from PanGu-α, we extend the dense Transfo
In the previous post, we introduced KV caching, a common optimization of the inference process of LLMs that make compute requirements of the (self-)attention mechanism to scale linearly rather than quadratically in the total sequence length (prompt + generated completions). More concretely, KV caching consists to spare the recomputation of key and value tensors of past tokens at each generation st
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