Meet EAGLE 3.1: The Speculative Decoding Algorithm That Fixes Attention Drift in LLM Inference
EAGLE 3.1 has been released, a collaborative effort by the EAGLE team, vLLM, and TorchSpec. This update addresses speculative decoding instability in large language model inference environments.
The EAGLE team, in collaboration with vLLM and TorchSpec, has announced the release of EAGLE 3.1.
This new version aims to rectify a critical issue in speculative decoding: attention drift.
By addressing this instability, EAGLE 3.1 seeks to improve the reliability and performance of large language model inference in production environments.
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