Alternatives and similar repositories for FLAP:

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

• yxli2123 / LoSparse
  ☆50Updated last year
• BaiTheBest / SparseLLM
  Official Repo for SparseLLM: Global Pruning of LLMs (NeurIPS 2024)
  ☆54Updated 3 weeks ago
• liyunqianggyn / Awesome-LLMs-Pruning
  Awesome LLM pruning papers all-in-one repository with integrating all useful resources and insights.
  ☆83Updated 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…
  ☆47Updated last year
• ruikangliu / IntactKV
  Official PyTorch implementation of "IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact"
  ☆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…
  ☆64Updated last year
• YouAreSpecialToMe / QST
  Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models
  ☆43Updated 5 months ago
• machilusZ / FastGen
  This repo contains the source code for: Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs
  ☆36Updated 8 months ago
• SqueezeAILab / SqueezedAttention
  SQUEEZED ATTENTION: Accelerating Long Prompt LLM Inference
  ☆46Updated 5 months ago
• ClubieDong / QAQ-KVCacheQuantization
  QAQ: Quality Adaptive Quantization for LLM KV Cache
  ☆49Updated last year
• zhangsichengsjtu / AFPQ
  AFPQ code implementation
  ☆20Updated last year
• luuyin / OWL
  Official Pytorch Implementation of "Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity"
  ☆64Updated 9 months ago
• ylsung / ECoFLaP
  Code for "ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models" (ICLR 2024)
  ☆19Updated last year
• hdong920 / LESS
  ☆50Updated 11 months ago
• sramshetty / ShortGPT
  Unofficial implementations of block/layer-wise pruning methods for LLMs.
  ☆68Updated 11 months ago
• aim-uofa / LoRAPrune
  ☆48Updated 4 months ago
• Intelligent-Computing-Lab-Yale / TesseraQ
  ☆20Updated 5 months ago
• VILA-Lab / GBLM-Pruner
  Are gradient information useful for pruning of LLMs?
  ☆43Updated 11 months ago
• cat538 / SKVQ
  SKVQ: Sliding-window Key and Value Cache Quantization for Large Language Models
  ☆19Updated 6 months ago
• Hsu1023 / DuQuant
  [NeurIPS 2024 Oral🔥] DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs.
  ☆156Updated 6 months ago
• DD-DuDa / BitDistiller
  [ACL 2024] A novel QAT with Self-Distillation framework to enhance ultra low-bit LLMs.
  ☆109Updated 11 months ago
• hahnyuan / ASVD4LLM
  Activation-aware Singular Value Decomposition for Compressing Large Language Models
  ☆62Updated 5 months ago
• ChenMnZ / PrefixQuant
  An algorithm for weight-activation quantization (W4A4, W4A8) of LLMs, supporting both static and dynamic quantization
  ☆127Updated 2 months ago
• ROIM1998 / APT
  [ICML'24 Oral] APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference
  ☆38Updated 10 months ago
• Equationliu / Kangaroo
  [NeurIPS 2024] The official implementation of "Kangaroo: Lossless Self-Speculative Decoding for Accelerating LLMs via Double Early Exitin…
  ☆51Updated 9 months ago
• sramshetty / mixture-of-depths
  An unofficial implementation of "Mixture-of-Depths: Dynamically allocating compute in transformer-based language models"
  ☆35Updated 10 months ago
• HuangOwen / RoLoRA
  [EMNLP 2024] RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization
  ☆33Updated 6 months ago
• Lucky-Lance / SPP
  [ICML 2024] SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models
  ☆20Updated 10 months ago
• NJUNLP / MCSD
  Multi-Candidate Speculative Decoding
  ☆35Updated 11 months ago
• VITA-Group / llm-kick
  [ICLR 2024] Jaiswal, A., Gan, Z., Du, X., Zhang, B., Wang, Z., & Yang, Y. Compressing llms: The truth is rarely pure and never simple.
  ☆23Updated last year