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Refining Jacobi Decoding for LLMs with Consistency-Based Fine-Tuning
- The article discusses a refined Jacobi decoding method for Large Language Models (LLMs) to improve efficiency and speed during inference.
- Existing methods like speculative decoding and Medusa have limitations, prompting the need for a more effective approach.
- Jacobi decoding method iteratively updates n-token sequences to converge to the output generated by autoregressive (AR) decoding.
- The proposed refinement aims to enhance LLMs to accurately predict multiple subsequent tokens with one step for faster convergence.
- The method involves training LLMs to map any state on the Jacobi trajectory to the fixed point efficiently.
- CLLMs (Consistency Large Language Models) are introduced, achieving significant speedup without additional memory costs.
- The fine-tuning process involves leveraging consistency loss and AR loss for improved generation quality and speed.
- Empirical results demonstrate 2.4× to 3.4× speed improvements in various benchmarks with CLLMs.
- CLLMs exhibit features like fast forwarding and stationary tokens, contributing to latency reduction and enhanced performance.
- The research presents CLLMs as a promising approach for optimizing LLM inference with minimal performance trade-offs.
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