AdaSPEC: Selective Knowledge Distillation for Efficient Speculative Decoders
Source: Dev.to
Introduction
AdaSPEC is a new method that speeds up large language models by using a small draft model for the initial generation pass, followed by verification and correction from the larger model.
How It Works
- Selective Knowledge Distillation: The draft model is trained only on the “easy” parts of the data, while the more difficult tokens are left for the large model to handle.
- Reference Helper: A helper component flags tricky words, allowing the draft model to focus on what it can reliably generate.
- Increased Acceptance Rate: By skipping hard‑to‑match words during training, the system accepts a larger portion of the draft, reducing the amount of work the large model must redo.
Benefits
- Faster Generation: Overall text is produced more quickly without sacrificing quality.
- Higher Quality: Experiments show that AdaSPEC often yields better results than previous speculative decoding methods.
- Broad Applicability: Effective on tasks such as simple arithmetic, short instructions, code snippets, and summarization.
Experimental Results
Tests demonstrate that AdaSPEC improves both speed and output quality across a variety of benchmarks, consistently outperforming older speculative decoding approaches.
Implications
AdaSPEC can significantly reduce latency for users seeking rapid AI responses, making large models more practical for deployment on devices like smartphones and web services.
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AdaSPEC: Selective Knowledge Distillation for Efficient Speculative Decoders
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