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arXiv:2307.09288cs.CL

Llama 2: Open Foundation and Fine-Tuned Chat Models

Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom

Abstract

In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.

arXiv comments
Journal reference
DOI
Subjects
cs.CLComputation and Language cs.AIArtificial Intelligence

On Hacker News

Linked by 7 identifiable commenters in 8 comments across 8 threads, from Oct 2023 to Jun 2026. Found in 1 HN submission from Jul 2023 to Jul 2023. Open submission discussions.

Comments linking this paper, per year.

Discussed in comments8 threads

  1. Are AI chatbots politically biased? 1 comment

    • Unsurprising result - here's Microsoft (in collaboration with MIT, Carnegie Mellon, and University of Washington) on how they measure & censor hate speech in their AIs [0]: Our ultimate aim is to shift power dynamics to targets of oppression. Therefore, we do not consider…

      like_any_otherView on HNParent context

  2. Russian Propaganda Has Now Infected Western AI Chatbots – New Study 1 comment

    • Microsoft (in collaboration with MIT, Carnegie Mellon, and University of Washington) on how they measure & censor hate speech in their AIs [0]: Our ultimate aim is to shift power dynamics to targets of oppression. Therefore, we do not consider identity dimensions that are…

      like_any_otherView on HNParent context

  3. AMD Announces "Instella" Open-Source 3B Language Models 1 comment

    • Right, the original LLaMA paper. LLaMA 2 and 3 are significantly more capable models trained on orders of magnitude more data, and those papers notably do not say where the data comes from. The LLaMA 3 paper helpfully mentions that "Much of the data we utilize is obtained from…

      raframView on HNParent context

  4. Not all 'open source' AI models are open: here's a ranking 1 comment

    • Facebook has been touting their work as both "open source" and "open science", despite being in direct violation of the terms [1, 2]. Others have done similar things, but not on such a big scale and with models with such big reach. In the community, the terms have now become…

      ninjinView on HNParent context

  5. How Meta trains large language models at scale 1 comment

    • Then, you should check out papers like https://arxiv.org/abs/2302.13971 and https://arxiv.org/abs/2307.09288 In the paper covering the original Llama they explicitly list their data sources in table 1 - including saying that they pretrained on the somewhat controversial books3…

      michaeltView on HNParent context

  6. Ask HN: AI/ML papers to catch up with current state of AI? 1 comment

    • Since nobody is actually recommending papers, here's an incomplete reading list that I sent out to some masters students I work with so they can understand the current research (academic) my little team is doing: Paper reference / main takeaways / link instructGPT / main…

      hapaninView on HNParent context

  7. RedPajama v2 Open Dataset with 30T Tokens for Training LLMs 1 comment

    • This is a lot of tokens. Llama 2 was trained on two trillion tokens [1] [1] https://arxiv.org/abs/2307.09288

      tydunnView on HNParent context

  8. How Transformers Work 1 comment

    • Ah, I see. You are unfamiliar with how SOTA language models are actually trained in current day, what RLHF is in detail, and how it differs from other training phases. I'd encourage you to read through the GPT3 [0] and llama2 [1] papers for more salient descriptions of the…

      hexagaView on HNParent context

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