Скачать или смотреть MatFormer: Explained. Nested Transformer for Elastic Inference. Foundation Models: LLMs.

GenAI Futures. MatFormer: Explained. Nested Transformer for Elastic Inference. Foundation Models: LLMs.

original paper: https://arxiv.org/pdf/2310.07707
original GitHub repo: https://github.com/devvrit/matformer/...

The provided educational podcast introduces MatFormer, a novel Transformer architecture designed for elastic inference across diverse computational constraints. It achieves this by incorporating a nested Feed Forward Network (FFN) block structure, allowing for the extraction of hundreds of accurate smaller models from a single, universally trained model without additional computational costs. The paper demonstrates MatFormer's effectiveness across language (MatLM) and vision (MatViT) models, showcasing improved validation loss, downstream evaluation, and significant inference time speedups through techniques like speculative decoding and adaptive retrieval. Unlike previous methods, MatFormer prioritizes optimizing a few nested granularities to yield an exponentially larger number of high-performing submodels.