yifanycc / lorettaLinks
[NAACL 24 Oral] LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models
☆34Updated 4 months ago
Alternatives and similar repositories for loretta
Users that are interested in loretta are comparing it to the libraries listed below
Sorting:
- [ICML 2024 Oral] This project is the official implementation of our Accurate LoRA-Finetuning Quantization of LLMs via Information Retenti…☆65Updated last year
- ☆15Updated 6 months ago
- SQUEEZED ATTENTION: Accelerating Long Prompt LLM Inference☆46Updated 6 months ago
- Activation-aware Singular Value Decomposition for Compressing Large Language Models☆68Updated 7 months ago
- Pytorch implementation of our paper accepted by ICML 2024 -- CaM: Cache Merging for Memory-efficient LLMs Inference☆39Updated 11 months ago
- Official Repo for SparseLLM: Global Pruning of LLMs (NeurIPS 2024)☆61Updated 2 months ago
- Awesome LLM pruning papers all-in-one repository with integrating all useful resources and insights.☆88Updated 5 months ago
- ☆23Updated 2 months ago
- ☆56Updated last year
- Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for LLMs☆82Updated 6 months ago
- ☆93Updated 2 weeks ago
- Official Pytorch Implementation of Our Paper Accepted at ICLR 2024-- Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLM…☆47Updated last year
- [ICML 2024] SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models☆21Updated last year
- [ICLR 2024] Jaiswal, A., Gan, Z., Du, X., Zhang, B., Wang, Z., & Yang, Y. Compressing llms: The truth is rarely pure and never simple.☆24Updated last month
- [ACL 2024] Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models☆89Updated last year
- [ICLR 2023] "Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers" by Tianlong Chen*, Zhenyu Zhang*, Ajay Jaiswal…☆51Updated 2 years ago
- [ICLR 2025] The official pytorch implement of "Dynamic Low-Rank Sparse Adaptation for Large Language Models".☆19Updated 2 months ago
- [ICML 2024] When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models☆30Updated 11 months ago
- ☆18Updated 6 months ago
- ☆9Updated 8 months ago
- Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs☆16Updated 5 months ago
- Official PyTorch implementation of "Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models"☆34Updated last week
- ☆54Updated 5 months ago
- Official code for the paper "Examining Post-Training Quantization for Mixture-of-Experts: A Benchmark"☆20Updated 11 months ago
- ☆47Updated 2 months ago
- Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models☆43Updated 7 months ago
- ☆83Updated last month
- [AAAI 2024] Fluctuation-based Adaptive Structured Pruning for Large Language Models☆51Updated last year
- Official PyTorch implementation of "IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact"☆44Updated last year
- D^2-MoE: Delta Decompression for MoE-based LLMs Compression☆48Updated 2 months ago