zyxxmu / DSnoTLinks
Official Pytorch Implementation of Our Paper Accepted at ICLR 2024-- Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs
☆49Updated last year
Alternatives and similar repositories for DSnoT
Users that are interested in DSnoT are comparing it to the libraries listed below
Sorting:
- [ICML'24 Oral] APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference☆44Updated last year
- ☆59Updated last year
- [ICLR 2025] Official implementation of paper "Dynamic Low-Rank Sparse Adaptation for Large Language Models".☆21Updated 5 months ago
- [ICML 2024 Oral] This project is the official implementation of our Accurate LoRA-Finetuning Quantization of LLMs via Information Retenti…☆67Updated last year
- ☆51Updated last year
- ☆57Updated 8 months ago
- [AAAI 2024] Fluctuation-based Adaptive Structured Pruning for Large Language Models☆59Updated last year
- Activation-aware Singular Value Decomposition for Compressing Large Language Models☆76Updated 10 months ago
- ☆20Updated 9 months ago
- [ICML 2024] SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models☆21Updated last year
- Official Repo for SparseLLM: Global Pruning of LLMs (NeurIPS 2024)☆64Updated 5 months ago
- Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs☆18Updated 8 months ago
- Official Pytorch Implementation of "Outlier-weighed Layerwise Sampling for LLM Fine-tuning" by Pengxiang Li, Lu Yin, Xiaowei Gao, Shiwei …☆34Updated 2 months ago
- [NeurIPS 2024 Oral🔥] DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs.☆165Updated 10 months ago
- A block pruning framework for LLMs.☆24Updated 3 months ago
- BESA is a differentiable weight pruning technique for large language models.☆17Updated last year
- LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning☆35Updated last year
- [EMNLP 2024] RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization☆37Updated 11 months ago
- [ICLR 2024 Spotlight] Code for the paper "Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy"☆92Updated 2 months ago
- ☆24Updated 8 months ago
- Pytorch implementation of our paper accepted by ICML 2024 -- CaM: Cache Merging for Memory-efficient LLMs Inference☆42Updated last year
- Github Repo for OATS: Outlier-Aware Pruning through Sparse and Low Rank Decomposition☆13Updated 4 months ago
- [ICML 2024] Junk DNA Hypothesis: A Task-Centric Angle of LLM Pre-trained Weights through Sparsity; Lu Yin*, Ajay Jaiswal*, Shiwei Liu, So…☆16Updated 4 months ago
- Awesome LLM pruning papers all-in-one repository with integrating all useful resources and insights.☆117Updated 3 weeks ago
- Towards Meta-Pruning via Optimal Transport, ICLR 2024 (Spotlight)☆16Updated 8 months ago
- ☆29Updated last year
- A family of efficient edge language models in 100M~1B sizes.☆15Updated 6 months ago
- ☆10Updated 11 months ago
- The code repository of "MBQ: Modality-Balanced Quantization for Large Vision-Language Models"☆55Updated 5 months ago
- This repo contains the source code for: Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs☆39Updated last year