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1. CASE-Lab-UMD/LLM-Drop CASE-Lab-UMD/LLM-Drop Public

   The official implementation of the paper "What Matters in Transformers? Not All Attention is Needed".

   Python 158 17

2. CASE-Lab-UMD/Unified-MoE-Compression CASE-Lab-UMD/Unified-MoE-Compression Public

   The official implementation of the paper "Demystifying the Compression of Mixture-of-Experts Through a Unified Framework".

   Python 56 5

3. CASE-Lab-UMD/Router-Tuning-Mixture-of-Depths CASE-Lab-UMD/Router-Tuning-Mixture-of-Depths Public

   The open-source Mixture of Depths code and the official implementation of the paper "Router-Tuning: A Simple and Effective Approach for Enabling Dynamic Depth in Transformers."

   Python 10 2

4. MEO MEO Public

   The source code of "Merging Experts into One: Improving Computational Efficiency of Mixture of Experts (EMNLP 2023)":

   Python 35 1

5. SparseAdapter SparseAdapter Public

   Source code of EMNLP 2022 Findings paper "SparseAdapter: An Easy Approach for Improving the Parameter-Efficiency of Adapters"

   Python 18

6. PAD-Net PAD-Net Public

   Source code of ACL 2023 Main Conference Paper "PAD-Net: An Efficient Framework for Dynamic Networks".

   Python 9