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We introduce LongCat-Flash, a 560-billion-parameter Mixture-of-Experts (MoE) language model designed for both computational efficiency and advanced agentic capabilities.
A History of Control Engineering 1930-1955
Stuart Bennett · 1993
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CLUE: A Chinese language understanding evaluation benchmark
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Staged training for transformer language models
Sheng Shen, Pete Walsh, Kurt Keutzer, Jesse Dodge, Matthew Peters, and Iz Beltagy · 2022
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ST-MoE: Designing stable and transferable sparse expert models
Barret Zoph, Irwan Bello, Sameer Kumar, Nan Du, Yanping Huang, Jeff Dean, Noam Shazeer, and William Fedus · 2022
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MultiPL-E: A scalable and extensible approach to benchmarking neural code generation
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A framework for the evaluation of code generation models
Loubna Ben Allal, Niklas Muennighoff, Logesh Kumar Umapathi, Ben Lipkin, and Leandro von Werra · 2022
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Self-instruct: Aligning language models with self-generated instructions
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Fast inference from transformers via speculative decoding
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Gqa: Training generalized multi-query transformer models from multi-head checkpoints
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Solar 10.7 b: Scaling large language models with simple yet effective depth up-scaling
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C-Eval: A multi-level multi-discipline chinese evaluation suite for foundation models
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