UtkarshSaxena1 / EigenAttnLinks
☆16Updated 9 months ago
Alternatives and similar repositories for EigenAttn
Users that are interested in EigenAttn are comparing it to the libraries listed below
Sorting:
- ☆15Updated 8 months ago
- ☆30Updated last year
- ☆33Updated last year
- AdaSplash: Adaptive Sparse Flash Attention (aka Flash Entmax Attention)☆15Updated this week
- [ICML 2024 Oral] This project is the official implementation of our Accurate LoRA-Finetuning Quantization of LLMs via Information Retenti…☆65Updated last year
- ☆30Updated 5 months ago
- [ICML'24 Oral] APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference☆43Updated last year
- ☆15Updated 9 months ago
- ☆18Updated 7 months ago
- [ICML 2024] SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models☆21Updated last year
- DELLA-Merging: Reducing Interference in Model Merging through Magnitude-Based Sampling☆33Updated last year
- Codes for Merging Large Language Models☆32Updated 11 months ago
- Official Pytorch Implementation of Our Paper Accepted at ICLR 2024-- Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLM…☆49Updated last year
- [ICLR 2024 Spotlight] Code for the paper "Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy"☆86Updated 3 weeks ago
- [NeurIPS 2024 Spotlight] Code and data for the paper "Finding Transformer Circuits with Edge Pruning".☆59Updated this week
- Official Implementation of FastKV: KV Cache Compression for Fast Long-Context Processing with Token-Selective Propagation☆21Updated last month
- ☆23Updated last month
- [ICML 2024] When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models☆31Updated last year
- ☆10Updated 10 months ago
- Code for the EMNLP24 paper "A simple and effective L2 norm based method for KV Cache compression."☆14Updated 7 months ago
- [EMNLP 2024] Quantize LLM to extremely low-bit, and finetune the quantized LLMs☆13Updated 11 months ago
- [ICML 2024] Unveiling and Harnessing Hidden Attention Sinks: Enhancing Large Language Models without Training through Attention Calibrati…☆40Updated last year
- ☆56Updated 7 months ago
- Code accompanying the paper "Massive Activations in Large Language Models"☆170Updated last year
- ☆36Updated 3 months ago
- The source code of "Merging Experts into One: Improving Computational Efficiency of Mixture of Experts (EMNLP 2023)":☆38Updated last year
- ☆38Updated 10 months ago
- ☆18Updated 6 months ago
- [NeurIPS 2024 Spotlight] EMR-Merging: Tuning-Free High-Performance Model Merging☆59Updated 4 months ago
- Official implementation of the paper: "A deeper look at depth pruning of LLMs"☆15Updated 11 months ago