yxli2123 / LoSparseLinks
☆58Updated last year
Alternatives and similar repositories for LoSparse
Users that are interested in LoSparse are comparing it to the libraries listed below
Sorting:
- Official Repo for SparseLLM: Global Pruning of LLMs (NeurIPS 2024)☆64Updated 3 months ago
- [ICML 2024 Oral] This project is the official implementation of our Accurate LoRA-Finetuning Quantization of LLMs via Information Retenti…☆65Updated last year
- [AAAI 2024] Fluctuation-based Adaptive Structured Pruning for Large Language Models☆56Updated last year
- Official Pytorch Implementation of Our Paper Accepted at ICLR 2024-- Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLM…☆49Updated last year
- SQUEEZED ATTENTION: Accelerating Long Prompt LLM Inference☆49Updated 7 months ago
- Activation-aware Singular Value Decomposition for Compressing Large Language Models☆74Updated 8 months ago
- [ICML'24 Oral] APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference☆43Updated last year
- Pytorch implementation of our paper accepted by ICML 2024 -- CaM: Cache Merging for Memory-efficient LLMs Inference☆41Updated last year
- ☆46Updated last year
- [ICML24] Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for LLMs☆89Updated 7 months ago
- ☆28Updated 11 months ago
- Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs☆18Updated 7 months ago
- This repo contains the source code for: Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs☆37Updated 11 months ago
- [ICLR 2025] The official pytorch implement of "Dynamic Low-Rank Sparse Adaptation for Large Language Models".☆19Updated 4 months ago
- [ICML 2024] SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models☆21Updated last year
- [ACL 2024] Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models☆94Updated last year
- ☆56Updated 7 months ago
- This pytorch package implements PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance (ICML 2022).☆46Updated 2 years ago
- The official implementation of the paper "Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques (TMLR)".☆71Updated 3 months ago
- An unofficial implementation of "Mixture-of-Depths: Dynamically allocating compute in transformer-based language models"☆35Updated last year
- [ICLR 2024 Spotlight] Code for the paper "Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy"☆86Updated 3 weeks ago
- Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding (EMNLP 2023 Long)☆60Updated 9 months ago
- ☆38Updated 10 months ago
- ☆50Updated 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.☆24Updated 2 months ago
- Official code for the paper "Examining Post-Training Quantization for Mixture-of-Experts: A Benchmark"☆20Updated 2 weeks ago
- Code for "ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models" (ICLR 2024)☆19Updated last year
- ☆18Updated 7 months ago
- [EMNLP 2024] RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization☆37Updated 9 months ago
- ☆26Updated 8 months ago