Pavlo Molchanov

A fresh approach to fine-tuning models enhances efficiency and accuracy in machine learning tasks.

New adaptable models can meet diverse needs without retraining.

This study explores methods to create smaller language models effectively and affordably.

This article analyzes model performance across various tasks and datasets.

A new method improves data quality for visual language models using augmentation techniques.

A method to shrink language models without sacrificing effectiveness through pruning and distillation.

A new method enhances LLM performance while reducing complexity.

Discover emerging techniques revolutionizing how machines see and understand images.

Researchers enhance language models for complex mathematical reasoning.