Seed Diffusion's high-speed characteristics stem from its innovative technical architecture:
- parallel decoding mechanismUnlike the autoregressive model that generates tokens one by one, it adopts a diffusion modeling framework, which reduces the number of generation steps by generating a draft of the whole and then refining it in parallel.
- Same strategy learning optimization: Training the model to learn to reach high quality generation in fewer diffusion steps.
- Structured Data Processing Advantages: The strong structure of the code itself is better suited to the iterative optimization of the diffusion model, allowing the model to converge to the desired output faster.
Empirical tests show that this architecture enables reasoning at 2146 tokens/s, which is 5.4 times faster than the traditional approach, providing users with a near-instant code generation experience.
This answer comes from the articleSeed Diffusion: Validating High-Speed Language Models for Next-Generation ArchitecturesThe
May not be reproduced without permission:AI productivity tools " Why is Seed Diffusion so much faster than traditional models?
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