Tencent's Hunyuan Hy4 overhauls its underlying layer, integrating DeepSeek's sparse attention and Zhipu's IndexCache.
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Beating AI Express News: Tencent’s Hunyuan Hy4 preview not only scales up the model size but also overhauls its underlying architecture. The previous Hy3 generation used traditional full attention, while Hy4 adopts Gated DSA, the gated version of DeepSeek Sparse Attention. When processing extremely long texts, the model no longer recalculates all previous content; instead, it first selects the most relevant parts for focused processing. Hy4 also adds IndexCache to DSA. Originally, each layer of the model had to re-judge which content is most important, but now some layers can directly reuse earlier selected results, cutting down on significant redundant computation. Tencent explicitly stated that this design is inspired by DeepSeek and GLM. Another change is the residual structure: traditional Transformers can be roughly viewed as having only one information backbone, while Hy4 uses iHC to expand this to four backbones, enabling the model to retain more information simultaneously in deep networks. The number of MoE (Mixture of Experts) experts has also increased from 192 in Hy3 to 256 routing experts plus 1 shared expert. Each token selects 8 routing experts and always passes through the shared expert.
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