Entries by Mehedi Hasan

Efficiently Fine-tuning Large Language Models with QLoRA: An Introductory Guide

Fine-tuning large language models (LLMs) such as LLaMA and T5 can produce impressive results, but the memory and hardware required for traditional 16-bit fine-tuning can be a major obstacle. A new method called QLoRA (Quantized Low-Rank Adapter) changes that, enabling efficient fine-tuning on large models using much less memory. This article simplifies the core concepts […]

Making Large Language Models Faster and More Energy Efficient with BitNet and bitnet.cpp

Large Language Models (LLMs) are becoming increasingly strong, but they also demand more computing power and energy. Researchers have created BitNet and its supporting framework, bitnet.cpp, to tackle these obstacles, providing a more intelligent approach to executing these models. In this article, we will explain the purpose of this innovative technology and how it can […]