haiquanlu / AlphaPruningLinks
[NeurIPS 2024] AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models
☆31Updated 8 months ago
Alternatives and similar repositories for AlphaPruning
Users that are interested in AlphaPruning are comparing it to the libraries listed below
Sorting:
- Data distillation benchmark☆72Updated 8 months ago
- [ICLR 2026] dParallel: Learnable Parallel Decoding for dLLMs☆58Updated 2 weeks ago
- Code for paper "Merging Multi-Task Models via Weight-Ensembling Mixture of Experts"☆30Updated last year
- [NeurIPS 2024 Spotlight] EMR-Merging: Tuning-Free High-Performance Model Merging☆76Updated 11 months ago
- Elucidated Dataset Condensation (NeurIPS 2024)☆20Updated last year
- CorDA: Context-Oriented Decomposition Adaptation of Large Language Models for task-aware parameter-efficient fine-tuning(NeurIPS 2024)☆53Updated last year
- Task Singular Vectors: Reducing Task Interference in Model Merging. Merge models avoiding task interference through separable models.☆48Updated last month
- [CVPR2024 highlight] Generalized Large-Scale Data Condensation via Various Backbone and Statistical Matching (G-VBSM)☆28Updated last year
- Official Pytorch Implementation of "Outlier-weighed Layerwise Sampling for LLM Fine-tuning" by Pengxiang Li, Lu Yin, Xiaowei Gao, Shiwei …☆35Updated 8 months ago
- [NeurIPS'25] dKV-Cache: The Cache for Diffusion Language Models☆129Updated 8 months ago
- The loss landscape of Large Language Models resemble basin!☆36Updated 7 months ago
- LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning☆36Updated last year
- [ICML'24 Oral] APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference☆46Updated last year
- ICLR 2024, Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching☆105Updated last year
- Awesome-Low-Rank-Adaptation☆128Updated 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
- The official implementation of "2024NeurIPS Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation"☆52Updated last year
- ☆63Updated last year
- ☆29Updated last year
- [ICML 2024] Unveiling and Harnessing Hidden Attention Sinks: Enhancing Large Language Models without Training through Attention Calibrati…☆46Updated last year
- ☆23Updated last year
- A curated list of Model Merging methods.☆96Updated 2 months ago
- Code accompanying the paper "Massive Activations in Large Language Models"☆195Updated last year
- [CVPR 2024] On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm☆81Updated 11 months ago
- [NeurIPS'25] The official code implementation for paper "R2R: Efficiently Navigating Divergent Reasoning Paths with Small-Large Model Tok…☆76Updated last week
- Code for paper "Parameter Efficient Multi-task Model Fusion with Partial Linearization"☆25Updated last year
- A generalized framework for subspace tuning methods in parameter efficient fine-tuning.☆171Updated 2 weeks ago
- [ICLR 2025] Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models☆153Updated 7 months ago
- (NeurIPS 2023 spotlight) Large-scale Dataset Distillation/Condensation, 50 IPC (Images Per Class) achieves the highest 60.8% on original …☆136Updated last year
- [NeurIPS 2025] VeriThinker: Learning to Verify Makes Reasoning Model Efficient☆64Updated 4 months ago