Alternatives and similar repositories for EigenAttn:

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

• ArminAzizi98 / LaMDA
  ☆13Updated 3 months ago
• VijayLingam95 / SVFT
  ☆26Updated last week
• MrGGLS / BlockPruner
  A block pruning framework for LLMs.
  ☆17Updated 8 months ago
• SempraETY / Pruning-via-Merging
  ☆17Updated 2 months ago
• yxli2123 / LoSparse
  ☆49Updated last year
• Shwai-He / MEO
  The source code of "Merging Experts into One: Improving Computational Efficiency of Mixture of Experts (EMNLP 2023)":
  ☆35Updated 10 months ago
• yangyifei729 / LaCo
  Official implementation for LaCo (EMNLP 2024 Findings)
  ☆12Updated 4 months ago
• princeton-nlp / Edge-Pruning
  [NeurIPS 2024 Spotlight] Code and data for the paper "Finding Transformer Circuits with Edge Pruning".
  ☆46Updated 2 months ago
• VITA-Group / Junk_DNA_Hypothesis
  [ICML 2024] Junk DNA Hypothesis: A Task-Centric Angle of LLM Pre-trained Weights through Sparsity; Lu Yin*, Ajay Jaiswal*, Shiwei Liu, So…
  ☆16Updated 8 months ago
• raymin0223 / fast_robust_early_exit
  Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding (EMNLP 2023 Long)
  ☆56Updated 4 months ago
• SqueezeAILab / SqueezedAttention
  SQUEEZED ATTENTION: Accelerating Long Prompt LLM Inference
  ☆40Updated 3 months ago
• Lucky-Lance / Expert_Sparsity
  [ACL 2024] Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models
  ☆82Updated 8 months ago
• hemingkx / SWIFT
  [ICLR 2025] SWIFT: On-the-Fly Self-Speculative Decoding for LLM Inference Acceleration
  ☆37Updated 2 months ago
• harveyhuang18 / EMR_Merging
  [NeurIPS 2024 Spotlight] EMR-Merging: Tuning-Free High-Performance Model Merging
  ☆44Updated 4 months ago
• hahnyuan / ASVD4LLM
  Activation-aware Singular Value Decomposition for Compressing Large Language Models
  ☆56Updated 4 months ago
• zyxxmu / DSnoT
  Official Pytorch Implementation of Our Paper Accepted at ICLR 2024-- Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLM…
  ☆43Updated 10 months ago
• htqin / IR-QLoRA
  [ICML 2024 Oral] This project is the official implementation of our Accurate LoRA-Finetuning Quantization of LLMs via Information Retenti…
  ☆60Updated 10 months ago
• Shwai-He / SparseAdapter
  Source code of EMNLP 2022 Findings paper "SparseAdapter: An Easy Approach for Improving the Parameter-Efficiency of Adapters"
  ☆18Updated 10 months ago
• ldery / Bonsai
  Code for "Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes"
  ☆27Updated 10 months ago
• r-three / smear
  ☆27Updated last year
• BaohaoLiao / ApiQ
  [EMNLP 2024] Quantize LLM to extremely low-bit, and finetune the quantized LLMs
  ☆12Updated 7 months ago
• VILA-Lab / GBLM-Pruner
  Are gradient information useful for pruning of LLMs?
  ☆42Updated 10 months ago
• GATECH-EIC / ACT
  [ICML 2024] Unveiling and Harnessing Hidden Attention Sinks: Enhancing Large Language Models without Training through Attention Calibrati…
  ☆31Updated 7 months ago
• Lucky-Lance / SPP
  [ICML 2024] SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models
  ☆18Updated 8 months ago
• hdong920 / GRIFFIN
  ☆36Updated 5 months ago
• hdong920 / LESS
  ☆49Updated 9 months ago