Seed Diffusion revolutionizes inference speed through parallel decoding, achieving a processing capacity of 2,146 characters (tokens) per second, 5.4 times faster than a traditional autoregressive model of the same size. This breakthrough is due to the unique generation method of the diffusion model: instead of sequential word-by-word output, the whole process is parallelized.
While maintaining high speed generation, the model's performance is comparable to autoregressive models of the same size on multiple code review benchmarks such as LiveCodeBench and Bigcode Bench. This high-speed performance makes it particularly suitable for development scenarios that require rapid iteration, providing developers with a near real-time code generation experience.
This answer comes from the articleSeed Diffusion: Validating High-Speed Language Models for Next-Generation ArchitecturesThe
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