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.
☆17Updated 8 months ago
Related projects ⓘ
Alternatives and complementary repositories for llm-kick
- Official Repo for SparseLLM: Global Pruning of LLMs (NeurIPS 2024)☆37Updated this week
- Pytorch implementation of our paper accepted by ICML 2024 -- CaM: Cache Merging for Memory-efficient LLMs Inference☆26Updated 5 months ago
- [ICML 2024 Oral] This project is the official implementation of our Accurate LoRA-Finetuning Quantization of LLMs via Information Retenti…☆59Updated 7 months ago
- An algorithm for static activation quantization of LLMs☆77Updated last week
- [EMNLP 2024] RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization☆21Updated last month
- Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for LLMs☆74Updated 5 months ago
- This repo contains the source code for: Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs☆32Updated 3 months ago
- The Official Implementation of Ada-KV: Optimizing KV Cache Eviction by Adaptive Budget Allocation for Efficient LLM Inference☆38Updated this week
- QAQ: Quality Adaptive Quantization for LLM KV Cache☆42Updated 7 months ago
- Official PyTorch implementation of IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact☆32Updated 5 months ago
- ☆46Updated last year
- 16-fold memory access reduction with nearly no loss☆59Updated last week
- [NeurIPS 2024 Oral🔥] DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs.☆110Updated last month
- AFPQ code implementation☆18Updated last year
- GEAR: An Efficient KV Cache Compression Recipefor Near-Lossless Generative Inference of LLM☆147Updated 4 months ago
- [AAAI 2024] Fluctuation-based Adaptive Structured Pruning for Large Language Models☆37Updated 10 months ago
- Unofficial implementations of block/layer-wise pruning methods for LLMs.☆51Updated 6 months ago
- [ICML'24 Oral] APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference☆28Updated 5 months ago
- Official Pytorch Implementation of "Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity"☆51Updated 4 months ago
- ☆31Updated this week
- Activation-aware Singular Value Decomposition for Compressing Large Language Models☆49Updated 3 weeks ago
- Official PyTorch implementation of FlatQuant: Flatness Matters for LLM Quantization☆63Updated last week
- Implementation of Kangaroo: Lossless Self-Speculative Decoding via Double Early Exiting☆44Updated 4 months ago
- ☆15Updated 3 weeks ago
- Code Repository of Evaluating Quantized Large Language Models☆104Updated 2 months ago
- Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models☆36Updated 2 weeks ago
- SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models☆24Updated 3 months ago
- ☆42Updated 7 months ago
- Code for "Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes"☆28Updated 7 months ago
- ☆45Updated 6 months ago