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Teaching Old LLMs New Tricks: The Consistency Model Makeover for Speed
- The article discusses enhancing Large Language Models (LLMs) speed through Consistency Large Language Models (CLLMs) and Jacobi decoding, focusing on greedy sampling strategies.
- Jacobi Decoding with KV Cache is explored as a technique to reduce iteration state length and save fixed tokens for attention computation.
- CLLMs are proposed to map any point on the Jacobi trajectory to a fixed point for increased speedups, akin to consistency models in diffusion models.
- The process involves data preparation, collection of Jacobi trajectories, data augmentation, post-processing, and training strategies for CLLMs.
- Training CLLMs involves optimizing losses to predict multiple tokens and maintain generation quality by outputting fixed points with minimal deviation.
- Acceleration mechanisms in CLLMs include fast-forwarding phenomena, stationary tokens, acquisition of linguistic concepts like collocations, and the integration of lookahead decoding for further speedups.
- The article details the experiments, evaluations, and limitations of CLLMs in optimizing LLMs for speed and efficiency.
- The study demonstrates the practical implications of utilizing consistency models and Jacobi decoding to accelerate LLMs, leading to significant improvements in generation speed.
- The combination of CLLMs with lookahead decoding is highlighted as a promising approach to further enhance decoding efficiency and accuracy.
- The article provides algorithms, illustrations, and comparisons to baseline algorithms to elucidate the advancements in LLM optimization for speed enhancement.
- The paper is available on arXiv under the CC0 1.0 Universal license.
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