This restricted access has limited researchers’ ability to understand how and why these large language models work, hindering progress on efforts to improve their robustness and mitigate known issues, such as bias, toxicity, and the potential for generating misinformation.

They are one of the clearest cases of the substantial potential benefits AI can offer at scale to billions of people.Įven with all the recent advancements in large language models, full research access to them remains limited because of the resources that are required to train and run such large models. Over the last year, large language models - natural language processing (NLP) systems with billions of parameters - have shown new capabilities to generate creative text, solve mathematical theorems, predict protein structures, answer reading comprehension questions, and more. We are making LLaMA available at several sizes (7B, 13B, 33B, and 65B parameters) and also sharing a LLaMA model card that details how we built the model in keeping with our approach to Responsible AI practices. Foundation models train on a large set of unlabeled data, which makes them ideal for fine-tuning for a variety of tasks. Training smaller foundation models like LLaMA is desirable in the large language model space because it requires far less computing power and resources to test new approaches, validate others’ work, and explore new use cases. Smaller, more performant models such as LLaMA enable others in the research community who don’t have access to large amounts of infrastructure to study these models, further democratizing access in this important, fast-changing field. UPDATE: We just launched Llama 2 - for more information on the latest see our blog post on Llama 2.Īs part of Meta’s commitment to open science, today we are publicly releasing LLaMA (Large Language Model Meta AI), a state-of-the-art foundational large language model designed to help researchers advance their work in this subfield of AI.

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