microsoft / MoonlitLinks
This is a collection of our research on efficient AI, covering hardware-aware NAS and model compression.
☆86Updated last year
Alternatives and similar repositories for Moonlit
Users that are interested in Moonlit are comparing it to the libraries listed below
Sorting:
- Official Repo for SparseLLM: Global Pruning of LLMs (NeurIPS 2024)☆67Updated 10 months ago
- [ICML 2024 Oral] Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs☆123Updated 7 months ago
- Code for the AAAI 2024 Oral paper "OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Model…☆68Updated last year
- [AAAI 2024] Fluctuation-based Adaptive Structured Pruning for Large Language Models☆69Updated 2 years ago
- [COLM 2025] Official PyTorch implementation of "Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models"☆67Updated 7 months ago
- Code for the NeurIPS 2022 paper "Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning".☆129Updated 2 years ago
- Code Repository of Evaluating Quantized Large Language Models☆136Updated last year
- An algorithm for weight-activation quantization (W4A4, W4A8) of LLMs, supporting both static and dynamic quantization☆172Updated 2 months ago
- LLM Inference with Microscaling Format☆34Updated last year
- QAQ: Quality Adaptive Quantization for LLM KV Cache☆55Updated last year
- ☆63Updated 2 years ago
- [ACL 2024] A novel QAT with Self-Distillation framework to enhance ultra low-bit LLMs.☆134Updated last year
- ☆157Updated 2 years ago
- This repo contains the source code for: Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs☆44Updated last year
- ☆52Updated last year
- Awesome LLM pruning papers all-in-one repository with integrating all useful resources and insights.☆147Updated 5 months ago
- ☆74Updated last month
- [ICML 2024 Oral] This project is the official implementation of our Accurate LoRA-Finetuning Quantization of LLMs via Information Retenti…☆67Updated last year
- Pytorch implementation of our paper accepted by ICML 2024 -- CaM: Cache Merging for Memory-efficient LLMs Inference☆48Updated last year
- [ICML 2025] SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models☆51Updated last year
- [NeurIPS 2024] The official implementation of "Kangaroo: Lossless Self-Speculative Decoding for Accelerating LLMs via Double Early Exitin…☆65Updated last year
- Activation-aware Singular Value Decomposition for Compressing Large Language Models☆88Updated last year
- GEAR: An Efficient KV Cache Compression Recipefor Near-Lossless Generative Inference of LLM☆176Updated last year
- ☆158Updated 11 months ago
- [EMNLP 2024] RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization☆37Updated last year
- ☆25Updated last year
- AFPQ code implementation☆23Updated 2 years ago
- [ICLR 2025] Palu: Compressing KV-Cache with Low-Rank Projection☆154Updated 11 months ago
- [NeurIPS 2024 Oral🔥] DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs.☆180Updated last year
- [ICML 2024] Official Implementation of SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks☆39Updated last year