Alternatives and similar repositories for L2_Compression:

Users that are interested in L2_Compression are comparing it to the libraries listed below

• zysxmu / DFSQ
  super-resolution; post-training quantization; model compression
  ☆11Updated last year
• chenbong / PSS-Net
  ☆16Updated 2 years ago
• shawnricecake / search-llm
  [NeurIPS 2024] Search for Efficient LLMs
  ☆12Updated 2 months ago
• ZiweiWangTHU / GMPQ
  This is the pytorch implementation for the paper: Generalizable Mixed-Precision Quantization via Attribution Rank Preservation, which is…
  ☆25Updated 3 years ago
• HuangOwen / QAT-ACS
  [TMLR] Official PyTorch implementation of paper "Efficient Quantization-aware Training with Adaptive Coreset Selection"
  ☆30Updated 7 months ago
• ModelTC / QLLM
  [ICLR 2024] This is the official PyTorch implementation of "QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Mod…
  ☆35Updated last year
• MAC-AutoML / OMPQ
  ☆25Updated 3 years ago
• megvii-research / TPS-CVPR2023
  ☆43Updated last year
• zysxmu / FDDA
  Pytorch implementation of our paper accepted by ECCV 2022-- Fine-grained Data Distribution Alignment for Post-Training Quantization
  ☆14Updated 2 years ago
• ThisisBillhe / torch_quantizer
  torch_quantizer is a out-of-box quantization tool for PyTorch models on CUDA backend, specially optimized for Diffusion Models.
  ☆22Updated 11 months ago
• MingSun-Tse / TPP
  [ICLR'23] Trainability Preserving Neural Pruning (PyTorch)
  ☆32Updated last year
• ofsoundof / random_channel_pruning
  ☆17Updated 2 years ago
• thu-nics / MBQ
  The code repository of "MBQ: Modality-Balanced Quantization for Large Vision-Language Models"
  ☆35Updated last week
• megvii-research / SSQL-ECCV2022
  PyTorch implementation of SSQL (Accepted to ECCV2022 oral presentation)
  ☆75Updated 2 years ago
• OpenGVLab / LLMPrune-BESA
  BESA is a differentiable weight pruning technique for large language models.
  ☆14Updated last year
• MingSun-Tse / Why-the-State-of-Pruning-so-Confusing
  [Preprint] Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Prunin…
  ☆40Updated 2 years ago
• nbasyl / OFQ
  The official implementation of the ICML 2023 paper OFQ-ViT
  ☆31Updated last year
• hustvl / PD-Quant
  [CVPR 2023] PD-Quant: Post-Training Quantization Based on Prediction Difference Metric
  ☆52Updated 2 years ago
• lliai / EMQ-series
  [ICCV-2023] EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization
  ☆25Updated last year
• zysxmu / IntraQ
  Pytorch implementation of our paper accepted by CVPR 2022 -- IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Sh…
  ☆31Updated 3 years ago
• Juanerx / Q-DiT
  [CVPR 2025] Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers
  ☆43Updated 6 months ago
• bytedance / MRECG
  ☆34Updated last year
• lmbxmu / CLR-RNF
  Pytorch implementation of our paper (TNNLS) -- Pruning Networks with Cross-Layer Ranking & k-Reciprocal Nearest Filters
  ☆12Updated 3 years ago
• snap-research / efficient-nn-tutorial
  Page for the CVPR 2023 Tutorial - Efficient Neural Networks: From Algorithm Design to Practical Mobile Deployments
  ☆12Updated last year
• MingSun-Tse / Good-DA-in-KD
  [NeurIPS'22] What Makes a "Good" Data Augmentation in Knowledge Distillation -- A Statistical Perspective
  ☆36Updated 2 years ago
• BillAmihom / RAPQ
  Pytorch implementation of RAPQ, IJCAI 2022
  ☆21Updated last year
• VITA-Group / UVC
  [ICLR 2022] "Unified Vision Transformer Compression" by Shixing Yu*, Tianlong Chen*, Jiayi Shen, Huan Yuan, Jianchao Tan, Sen Yang, Ji Li…
  ☆52Updated last year
• MingSun-Tse / SRP
  [ICLR'22] PyTorch code for our paper "Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning"
  ☆27Updated 2 years ago
• Daner-Wang / VTC-LFC
  ☆26Updated 2 years ago
• WeixiangXu / STTN
  ☆17Updated 2 years ago