English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

LoRA is a technique that reduces the number of trainable parameters in aneural network by introducing low-rank adapters to linear layers. Thistechnique is used both for fine-tuning (LoRA, QLoRA) and full train (ReLoRA).This paper presents the RunLoRA framework for efficient implementations of LoRAthat significantly improves the speed of neural network training andfine-tuning using low-rank adapters. The proposed implementation optimizes thecomputation of LoRA operations based on dimensions of corresponding linearlayer, layer input dimensions and lora rank by choosing best forward andbackward computation graph based on FLOPs and time estimations, resulting infaster training without sacrificing accuracy. The experimental results show upto 17% speedup on Llama family of models.

Run LoRA Run: Faster and Lighter LoRA Implementations