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☆45Updated last year
- ☆59Updated last year
- Activation-aware Singular Value Decomposition for Compressing Large Language Models☆78Updated 11 months ago
- [AAAI 2024] Fluctuation-based Adaptive Structured Pruning for Large Language Models☆61Updated last year
- [ICLR 2025] Official implementation of paper "Dynamic Low-Rank Sparse Adaptation for Large Language Models".☆21Updated 6 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
- [ICML 2024] SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models☆21Updated last year
- ☆20Updated 9 months ago
- Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs☆18Updated 9 months ago
- ☆51Updated last year
- Official Repo for SparseLLM: Global Pruning of LLMs (NeurIPS 2024)☆65Updated 5 months ago
- ☆58Updated 9 months ago
- [ICML24] Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for LLMs☆92Updated 9 months ago
- A family of efficient edge language models in 100M~1B sizes.☆17Updated 7 months ago
- Awesome LLM pruning papers all-in-one repository with integrating all useful resources and insights.☆119Updated last month
- [EMNLP 2024] RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization☆38Updated 11 months ago
- [NeurIPS 2024 Oral🔥] DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs.☆166Updated 11 months ago
- BESA is a differentiable weight pruning technique for large language models.☆17Updated last year
- [COLM 2025] Official PyTorch implementation of "Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models"☆51Updated 2 months ago
- [ICLR 2024] This is the official PyTorch implementation of "QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Mod…☆39Updated last year
- The code repository of "MBQ: Modality-Balanced Quantization for Large Vision-Language Models"☆56Updated 6 months ago
- This repo contains the source code for: Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs☆39Updated last year
- Github Repo for OATS: Outlier-Aware Pruning through Sparse and Low Rank Decomposition☆13Updated 5 months ago
- Official Pytorch Implementation of "Outlier-weighed Layerwise Sampling for LLM Fine-tuning" by Pengxiang Li, Lu Yin, Xiaowei Gao, Shiwei …☆34Updated 3 months ago
- ☆22Updated 10 months ago
- [ACL 2024] Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models☆101Updated last year
- Are gradient information useful for pruning of LLMs?☆46Updated 3 weeks ago
- ☆11Updated last year
- Official Pytorch Implementation of "Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity"☆73Updated 2 months ago
- LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning☆35Updated last year