htqin / IR-QLoRALinks
[ICML 2024 Oral] This project is the official implementation of our Accurate LoRA-Finetuning Quantization of LLMs via Information Retention
☆67Updated last year
Alternatives and similar repositories for IR-QLoRA
Users that are interested in IR-QLoRA are comparing it to the libraries listed below
Sorting:
- Activation-aware Singular Value Decomposition for Compressing Large Language Models☆80Updated last year
- ☆61Updated 2 years ago
- [ICML24] Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for LLMs☆95Updated 11 months ago
- Official Repo for SparseLLM: Global Pruning of LLMs (NeurIPS 2024)☆67Updated 7 months ago
- [EMNLP 2024] RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization☆38Updated 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
- [ICML 2024] Official Implementation of SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks☆37Updated 9 months ago
- [AAAI 2024] Fluctuation-based Adaptive Structured Pruning for Large Language Models☆63Updated last year
- [ACL 2024] Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models☆109Updated last year
- ☆23Updated last year
- Pytorch implementation of our paper accepted by ICML 2024 -- CaM: Cache Merging for Memory-efficient LLMs Inference☆47Updated last year
- ☆10Updated last year
- [COLM 2025] Official PyTorch implementation of "Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models"☆57Updated 4 months ago
- ☆60Updated 11 months ago
- This repo contains the source code for: Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs☆41Updated last year
- [ICLR 2025] Official implementation of paper "Dynamic Low-Rank Sparse Adaptation for Large Language Models".☆23Updated 8 months ago
- [CoLM'25] The official implementation of the paper <MoA: Mixture of Sparse Attention for Automatic Large Language Model Compression>☆150Updated 4 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
- ☆30Updated last year
- An unofficial implementation of "Mixture-of-Depths: Dynamically allocating compute in transformer-based language models"☆36Updated last year
- [ICLR‘24 Spotlight] Code for the paper "Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy"☆97Updated 4 months ago
- [ICML'24 Oral] APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference☆46Updated last year
- [EMNLP 2024] Quantize LLM to extremely low-bit, and finetune the quantized LLMs☆15Updated last year
- ☆23Updated 11 months ago
- [ICLR 2025] Mixture Compressor for Mixture-of-Experts LLMs Gains More☆61Updated 9 months ago
- Are gradient information useful for pruning of LLMs?☆47Updated 2 months ago
- Unofficial implementations of block/layer-wise pruning methods for LLMs.☆72Updated last year
- D^2-MoE: Delta Decompression for MoE-based LLMs Compression☆69Updated 7 months ago
- [ICLR 2024] This is the official PyTorch implementation of "QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Mod…☆30Updated last year
- [ICML 2024] SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models☆21Updated last year