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而今年1月最新发布的Kimi K2.5模型，则成为月之暗面近期收入暴涨的导火索。
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I wanted to test this claim with SAT problems. Why SAT? Because solving SAT problems require applying very few rules consistently. The principle stays the same even if you have millions of variables or just a couple. So if you know how to reason properly any SAT instances is solvable given enough time. Also, it's easy to generate completely random SAT problems that make it less likely for LLM to solve the problem based on pure pattern recognition. Therefore, I think it is a good problem type to test whether LLMs can generalize basic rules beyond their training data.
int8 — 质量和大小之间的平衡。质量损失极小（约 1~3%），文件大小比 FP16 减少约 2 倍。