Intelligent-Computing-Lab-Yale / TesseraQ
☆13Updated last week
Related projects ⓘ
Alternatives and complementary repositories for TesseraQ
- [EMNLP 2024] RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization☆21Updated last month
- [ICLR 2024] This is the official PyTorch implementation of "QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Mod…☆35Updated 8 months ago
- AFPQ code implementation☆18Updated last year
- ☆17Updated last week
- torch_quantizer is a out-of-box quantization tool for PyTorch models on CUDA backend, specially optimized for Diffusion Models.☆17Updated 7 months ago
- Official implementation of the EMNLP23 paper: Outlier Suppression+: Accurate quantization of large language models by equivalent and opti…☆42Updated last year
- The official implementation of the ICML 2023 paper OFQ-ViT☆27Updated last year
- An algorithm for static activation quantization of LLMs☆68Updated this week
- The official PyTorch implementation of the NeurIPS2022 (spotlight) paper, Outlier Suppression: Pushing the Limit of Low-bit Transformer L…☆46Updated 2 years ago
- Official Repo for SparseLLM: Global Pruning of LLMs (NeurIPS 2024)☆36Updated 3 weeks ago
- Pytorch implementation of our paper accepted by ICML 2024 -- CaM: Cache Merging for Memory-efficient LLMs Inference☆25Updated 4 months ago
- [TMLR] Official PyTorch implementation of paper "Efficient Quantization-aware Training with Adaptive Coreset Selection"☆29Updated 2 months ago
- BESA is a differentiable weight pruning technique for large language models.☆14Updated 8 months ago
- Official Pytorch Implementation of Our Paper Accepted at ICLR 2024-- Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLM…☆36Updated 7 months ago
- Official PyTorch implementation of FlatQuant: Flatness Matters for LLM Quantization☆59Updated this week
- Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models☆35Updated last week
- [NeurIPS 2023] ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformer☆31Updated 11 months ago
- PyTorch code for Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers☆33Updated 2 months ago
- [ICML 2024 Oral] This project is the official implementation of our Accurate LoRA-Finetuning Quantization of LLMs via Information Retenti…☆59Updated 6 months ago
- [AAAI 2024] Fluctuation-based Adaptive Structured Pruning for Large Language Models☆37Updated 10 months ago
- [ICLR 2024] Jaiswal, A., Gan, Z., Du, X., Zhang, B., Wang, Z., & Yang, Y. Compressing llms: The truth is rarely pure and never simple.☆17Updated 7 months ago
- Code for the AAAI 2024 Oral paper "OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Model…☆53Updated 8 months ago
- [ICLR 2024] This is the official PyTorch implementation of "QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Mod…☆19Updated 8 months ago
- [Preprint] Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Prunin…☆40Updated last year
- Activation-aware Singular Value Decomposition for Compressing Large Language Models☆49Updated 3 weeks ago
- ☆24Updated 7 months ago
- This project is the official implementation of our accepted IEEE TPAMI paper Diverse Sample Generation: Pushing the Limit of Data-free Qu…☆14Updated last year
- Official implementation of the ICLR 2024 paper AffineQuant☆21Updated 7 months ago
- ☆17Updated 3 months ago