Summary of Channel Merging: Preserving Specialization For Merged Experts, by Mingyang Zhang et al.
Channel Merging: Preserving Specialization for Merged Experts
by Mingyang Zhang, Jing Liu, Ganggui Ding, Xinyi Yu, Linlin Ou, Bohan Zhuang
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents an innovative approach to improving the performance of large language models (LLMs) while reducing memory footprint during inference. By integrating diverse LLMs and applying traditional ensemble methods, the overall competency of LLMs is significantly boosted. However, these methods can be memory-intensive, leading to inefficiencies. To address this challenge, model merging strategies have emerged, merging all LLMs into one model to reduce memory footprint. Despite advances in model merging, previous methods to mitigate parameter conflicts and performance decline, such as post-pruning and partial merging, have limitations. The authors introduce Channel Merging, a novel strategy that clusters and merges channel parameters based on their similarity to form several groups offline. This approach minimizes parameter conflicts while enhancing storage efficiency during inference. Experimental results demonstrate that Channel Merging delivers high performance, matching unmerged models in tasks like English and Chinese reasoning, mathematical reasoning, and code generation, with comparable results to model ensemble using a task-specific router. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) have been fine-tuned for various tasks to improve their performance. By combining different LLMs, we can make them even better. However, this approach requires a lot of memory, which can be a problem. To solve this issue, researchers have developed ways to merge all the LLMs into one model that uses less memory. While these methods work well, they can also cause problems like conflicts between different models and a decrease in performance as more experts are added. The authors introduce a new way to merge models called Channel Merging. This method groups similar parameters together and reduces the number of conflicts during inference. Tests show that Channel Merging works well and is comparable to other methods. |
Keywords
» Artificial intelligence » Inference » Pruning
