CASE-Lab-UMD / Router-Tuning-Mixture-of-DepthsLinks
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. (EMNLP 2025)"
☆16Updated 2 weeks ago
Alternatives and similar repositories for Router-Tuning-Mixture-of-Depths
Users that are interested in Router-Tuning-Mixture-of-Depths are comparing it to the libraries listed below
Sorting:
- Official Repo for SparseLLM: Global Pruning of LLMs (NeurIPS 2024)☆66Updated 6 months ago
- Pytorch implementation of our paper accepted by ICML 2024 -- CaM: Cache Merging for Memory-efficient LLMs Inference☆47Updated last year
- Activation-aware Singular Value Decomposition for Compressing Large Language Models☆80Updated 11 months ago
- [ICML'24 Oral] APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference☆45Updated last year
- ☆14Updated 11 months ago
- D^2-MoE: Delta Decompression for MoE-based LLMs Compression☆68Updated 6 months ago
- Official implementation of the paper: "A deeper look at depth pruning of LLMs"☆15Updated last year
- Official Pytorch Implementation of Our Paper Accepted at ICLR 2024-- Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLM…☆50Updated last year
- [EMNLP 2024] RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization☆38Updated last year
- Unofficial implementations of block/layer-wise pruning methods for LLMs.☆72Updated last year
- A block pruning framework for LLMs.☆27Updated 5 months ago
- [ACL 2025] Squeezed Attention: Accelerating Long Prompt LLM Inference☆54Updated 10 months ago
- The code for "AttentionPredictor: Temporal Pattern Matters for Efficient LLM Inference", Qingyue Yang, Jie Wang, Xing Li, Zhihai Wang, Ch…☆19Updated 3 months ago
- The official implementation of the paper "Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques (TMLR)".☆78Updated 7 months ago
- This repo contains the source code for: Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs☆40Updated last year
- [ICML 2024 Oral] This project is the official implementation of our Accurate LoRA-Finetuning Quantization of LLMs via Information Retenti…☆67Updated last year
- [COLM 2025] Official PyTorch implementation of "Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models"☆55Updated 3 months ago
- Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models☆48Updated 11 months ago
- [ICLR 2025] Official implementation of paper "Dynamic Low-Rank Sparse Adaptation for Large Language Models".☆23Updated 7 months ago
- ☆29Updated 4 months ago
- ☆20Updated 10 months ago
- The Official Implementation of Ada-KV [NeurIPS 2025]☆105Updated 3 weeks ago
- Implementation for the paper: CMoE: Fast Carving of Mixture-of-Experts for Efficient LLM Inference☆25Updated 7 months ago
- Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs☆21Updated 10 months ago
- Official Implementation of FastKV: KV Cache Compression for Fast Long-Context Processing with Token-Selective Propagation☆24Updated 5 months ago
- [AAAI 2024] Fluctuation-based Adaptive Structured Pruning for Large Language Models☆61Updated last year
- ☆61Updated 2 years ago
- [ACL 2024] Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models☆105Updated last year
- [ICML24] Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for LLMs☆94Updated 10 months ago
- Official code for the paper "Examining Post-Training Quantization for Mixture-of-Experts: A Benchmark"☆22Updated 3 months ago