For a long time, researchers have sought artificial intelligence (AI) that matches or exceeds
human intelligence. AI agents, which are artificial entities capable of sensing the …

Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

Instruction tuning large language models (LLMs) using machine-generated instruction-
following data has been shown to improve zero-shot capabilities on new tasks, but the idea …

We present QLoRA, an efficient finetuning approach that reduces memory usage enough to
finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

While large-scale unsupervised language models (LMs) learn broad world knowledge and
some reasoning skills, achieving precise control of their behavior is difficult due to the …

Modern artificial intelligence (AI) systems are powered by foundation models. This paper
presents a new set of foundation models, called Llama 3. It is a herd of language models …

How do large language models (LLMs) develop and evolve over the course of training?
How do these patterns change as models scale? To answer these questions, we introduce …

Large language models are trained in two stages:(1) unsupervised pretraining from raw text,
to learn general-purpose representations, and (2) large scale instruction tuning and …

Prior work has shown that finetuning large language models (LLMs) using machine-
generated instruction-following data enables such models to achieve remarkable zero-shot …