After soaring for years, Tesla’s stock was down more than 15 percent for the day on Monday and has tumbled more than 50% from its recent high in mid-December.

U.S. President Donald Trump said on Monday he would hold Iran responsible for any attacks carried out by the Houthi group that it backs in Yemen, as his administration expanded the biggest U.S ...

There must be something in the water at Wasl 51. New to Time Out Dubai’s top 10 MENA restaurants in Dubai is Sufret Maryam, the latest creation from Salam Dakkak, chef-owner of Dubai institution Bait ...

Semtech LR2021, the first chip in the LoRa Plus family, supports LoRa Gen 4 technology with both terrestrial and satellite networks in the Sub-GHz, 2.4 GHz ISM bands, and licensed S-band, while ...

Abstract: Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning approach that adaptes pre-trained Large Language Models (LLMs) to multi-tenant tasks by generating a variety of LoRA adapters.

The Middle East and North Africa region is poised for a strong year of initial public offerings in 2025, with Saudi Arabia set to dominate the market, according to recent analysis. With 27 potential ...

Researchers are exploring parameter-efficient fine-tuning (PEFT) techniques such as LoRA (Low-Rank Adaptation) and QLoRA (Quantized LoRA) to reduce computational demands while maintaining accuracy.

Thank you for reporting this station. We will review the data in question. You are about to report this weather station for bad data. Please select the information that is incorrect.

Analysts mull if this isn’t the beginning of the end for the ‘rules-based order’, long accused of hypocrisy.

The rapid growth of web content presents a challenge for efficiently extracting and summarizing relevant information. In this tutorial, we demonstrate how to leverage Firecrawl for web scraping and ...

LLMs are widely used for conversational AI, content generation, and enterprise automation. However, balancing performance with computational efficiency is a key challenge in this field. Many ...