UNITES-Lab / MoE-QuantizationLinks
Official code for the paper "Examining Post-Training Quantization for Mixture-of-Experts: A Benchmark"
☆27Updated 6 months ago
Alternatives and similar repositories for MoE-Quantization
Users that are interested in MoE-Quantization are comparing it to the libraries listed below
Sorting:
- Activation-aware Singular Value Decomposition for Compressing Large Language Models☆83Updated last year
- This repo contains the source code for: Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs☆43Updated last year
- An algorithm for weight-activation quantization (W4A4, W4A8) of LLMs, supporting both static and dynamic quantization☆170Updated last month
- Pytorch implementation of our paper accepted by ICML 2024 -- CaM: Cache Merging for Memory-efficient LLMs Inference☆47Updated last year
- GEAR: An Efficient KV Cache Compression Recipefor Near-Lossless Generative Inference of LLM☆175Updated last year
- [ICML 2024] Official Implementation of SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks☆38Updated 11 months ago
- ☆157Updated 10 months ago
- Official Repo for SparseLLM: Global Pruning of LLMs (NeurIPS 2024)☆67Updated 9 months ago
- ☆31Updated last year
- The official implementation of the paper "Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques (TMLR)".☆88Updated 9 months ago
- Official Pytorch Implementation of "Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity"☆75Updated 6 months ago
- [CoLM'25] The official implementation of the paper <MoA: Mixture of Sparse Attention for Automatic Large Language Model Compression>☆153Updated last month
- ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization☆111Updated last year
- [ICLR 2024] Jaiswal, A., Gan, Z., Du, X., Zhang, B., Wang, Z., & Yang, Y. Compressing llms: The truth is rarely pure and never simple.☆26Updated 8 months ago
- ☆133Updated 7 months ago
- ☆51Updated last year
- [ICML 2025] SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models☆47Updated last year
- [ICLR 2025] Palu: Compressing KV-Cache with Low-Rank Projection☆151Updated 10 months ago
- [ICLR‘24 Spotlight] Code for the paper "Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy"☆102Updated 6 months ago
- [ACL 2024] Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models☆113Updated last year
- [NeurIPS 24 Spotlight] MaskLLM: Learnable Semi-structured Sparsity for Large Language Models☆183Updated last year
- An unofficial implementation of "Mixture-of-Depths: Dynamically allocating compute in transformer-based language models"☆36Updated last year
- Unofficial implementations of block/layer-wise pruning methods for LLMs.☆75Updated last year
- [ICML24] Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for LLMs☆98Updated last year
- Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs☆22Updated 2 months ago
- [ACL 2025] Squeezed Attention: Accelerating Long Prompt LLM Inference☆55Updated last year
- 16-fold memory access reduction with nearly no loss☆109Updated 9 months ago
- [COLM 2025] Official PyTorch implementation of "Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models"☆64Updated 6 months ago
- [ICML 2024] KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache☆346Updated last month
- ☆30Updated last year