A curated list for Efficient Large Language Models

Model compression toolkit engineered for enhanced usability, comprehensiveness, and efficiency.

[ICML24] Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for LLMs

D^2-MoE: Delta Decompression for MoE-based LLMs Compression

[ICLR 2024] Jaiswal, A., Gan, Z., Du, X., Zhang, B., Wang, Z., & Yang, Y. Compressing llms: The truth is rarely pure and never simple.

LLM Compression Benchmark

LLM Inference on AWS Lambda

papers of llm compression

[CAAI AIR'24] Minimize Quantization Output Error with Bias Compensation

Interpretation code for analyzing LLMs compression effects for the paper "When Reasoning Meets Compression: Understanding the Effects of LLMs Compression on Large Reasoning Models"

A standard PyTorch implementation of Google’s paper Language Modeling Is Compression—with no reliance on Haiku or JAX. Drawing on the original repository (https://github.com/google-deepmind/language_modeling_is_compression), this code is capable of reproducing the key results from the paper.

Token Price Estimation for LLMs

NYCU Edge AI Final Project Using SGLang

Research code for LLM Compression using Functional Algorithms, exploring stratified manifold learning, clustering, and compression techniques. Experiments span synthetic datasets (Swiss Roll, Manifold Singularities) and real-world text embeddings (DBpedia-14). The goal is to preserve semantic structure while reducing model complexity.