REAM: Merging Improves Pruning of Experts in LLMs

1. 1Identify experts in the MoE model that can be merged
2. 2Apply router-weighted expert activation pruning (REAP) to select experts for merging
3. 3Merge selected experts to reduce model parameters
4. 4Evaluate the performance of the merged model to ensure minimal accuracy loss

AI engineers and researchers working on large language models can benefit from this technique to optimize model deployment, while ML researchers can apply these findings to improve model efficiency

💡 Merging experts in MoE models can be an effective way to prune parameters and reduce memory requirements

💡 REAM: merging experts in LLMs reduces memory requirements without sacrificing accuracy