pxlksr / llama-3-70b-instruct-gradient-524k

该模型将Llama-3 70B的上下文长度从8k扩展到> 524K，由Gradient开发，由Crusoe Energy的算力资助。[Crusoe Energy 的链接](https://hugging-face.cn/crusoeai)。它表明，SOTA LLMs可以通过适当地调整RoPE theta来以最小训练在长上下文中运行。我们在这个阶段训练了2.1亿个token，全部阶段总共大约4亿个token，这是Llama-3原始预训练数据的<0.003%。

方法

meta-llama/Meta-Llama-3-70B-Instruct 作为基础
NTK感知的插值[4]，根据缩放定律[2]设置RoPE theta的最佳进度
在增加的上下文长度上进行渐进式训练，类似于Large World Model 1

Infrastructure

我们基于EasyContext Blockwise RingAttention库[5]，在Crusoe Energy高性能L40S集群上可扩展且高效地训练非常长的上下文。

我们通过定制的网络拓扑在环型注意力上叠加并行性，以更好地利用大型GPU集群，应对设备间传递大量KV块时的网络瓶颈。

Data

对于训练数据，我们通过增强SlimPajama来生成长上下文。我们也基于UltraChat[3]的聊天数据集微调，按照与[1]类似的数据增强配方。

渐进式训练细节

	65K	262K	524K
初始化来源	Llama-3-70B-Instruct	65K	262K
序列长度 2^N	16	18	19
RoPE theta	15296098	207112184	1062356830
批次大小	64	16	8
梯度累积步数	1	1	2
步骤	20	25	25
总token数	83886080	104857600	209715200
学习率	2.00E-05	2.00E-05	2.00E-05
GPU数量	512	512	512
GPU类型	NVIDIA L40S	NVIDIA L40S	NVIDIA L40S
训练时间（墙时间）	100	170	284

评估细节

EVAL_MAX_CONTEXT_LENGTH=640000
EVAL_MIN_CONTEXT_LENGTH=100
EVAL_CONTEXT_INTERVAL=32000
EVAL_DEPTH_INTERVAL=0.2
EVAL_NUM_SAMPLES=2
EVAL_RND_NUMBER_DIGITS=8
HAYSTACK:
    EVAL_GENERATOR_TOKENS=925000

Haystack是“haystack 3”，在此博客文章中有更详细的介绍。

量

The Gradient AI 团队

https://gradient.ai/

Gradient正在加速各行各业的AI转型。我们的AI Foundry通过整合您数据来部署自动化助手，支持您业务中的关键操作。

联系我们

发送邮件至 contact@gradient.ai

参考文献

[1] Liu, Hao, et al. “World Model on Million-Length Video And Language With RingAttention.” arXiv preprint arXiv:2402.08268 (2024).

[2] Liu, Xiaoran, et al. “Scaling laws of rope-based extrapolation.” arXiv preprint arXiv:2310.05209 (2023).

[3] Ning Ding, Yulin Chen, Bokai Xu, Yujia Qin, Zhi Zheng, Shengding Hu, Zhiyuan
Liu, Maosong Sun, and Bowen Zhou. Enhancing chat language models by scaling
high-quality instructional conversations. arXiv preprint arXiv:2305.14233, 2023.

[4] Peng, Bowen, et al. “Yarn: Efficient context window extension of large language models.” arXiv preprint arXiv:2309.00071 (2023).

[5] https://github.com/jzhang38/EasyContext

基础模型

模型细节

Meta 开发了并发布了 Meta Llama 3 系列大型语言模型（LLM），这是一组大小为 8B 和 70B 的预训练和指令微调生成文本模型。Llama 3 指令微调模型针对对话场景进行了优化，并在常见的行业基准测试中优于许多可用的开源聊天模型。此外，在开发这些模型时，我们格外注重优化帮助性和安全性。

模型开发者 Meta

变体 Llama 3 有两种大小——8B 和 70B 参数，包括预训练和指令微调版本。

输入模型仅接受文本输入。

输出模型仅生成文本和代码。

模型架构 Llama 3 是一个自回归语言模型，采用优化的 Transformer 架构。微调版本使用监督式微调（SFT）和人类反馈强化学习（RLHF）与人类的友好性和安全性偏好相一致。

	训练数据	Params	上下文长度	GQA	标记计数	知识截止时间
Llama 3	一种新的公开在线数据混合。	8B	8k	是	15T+	2023年3月
Llama 3	一种新的公开在线数据混合。	70B	8k	是	15T+	2023年12月

Llama 3 系列模型。标记计数仅指预训练数据。8 和 70B 版本都使用分组查询关注（GQA）来提高推理的可扩展性。

模型发布日期 2024年4月18日。

状态这是一个在离线数据集上训练的静态模型。随着我们通过社区反馈提高模型安全性，未来将发布微调模型的版本。

许可可在以下链接获取定制商业许可：[https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)

如何提出问题和评论关于该模型有关如何提供模型反馈或评论的说明可在模型的 [README](https://github.com/meta-llama/llama3) 中找到。有关生成参数的技术信息以及如何将 Llama 3 用于应用程序的食谱，请参阅 [这里](https://github.com/meta-llama/llama-recipes)。

预期用途

预期用途案例 Llama 3 预计用于英文的商业和研究用途。指令微调模型用于类似助手的聊天，而预训练模型可以适应各种自然语言生成任务。

范围外 违法使用任何违反适用法律或法规的方式（包括贸易合规法规）。使用其他任何违反可接受使用政策和 Llama 3 社区许可证的方式。使用除英语之外的语言**。

**注意：开发者可以微调超出英语的 Llama 3 模型，前提是它们符合 Llama 3 社区许可证和可接受使用政策**。

如何使用

该存储库包含两个版本的 Meta-Llama-3-70B-Instruct，用于与 transformers 和原始 llama3 代码库一起使用。

与 transformers 使用

请参考以下与 Transformers 一起使用的示例片段

import transformers
import torch

model_id = "meta-llama/Meta-Llama-3-70B-Instruct"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="auto",
)

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]

prompt = pipeline.tokenizer.apply_chat_template(
		messages, 
		tokenize=False, 
		add_generation_prompt=True
)

terminators = [
    pipeline.tokenizer.eos_token_id,
    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = pipeline(
    prompt,
    max_new_tokens=256,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])

与 `llama3` 使用

请按照 [repository](https://github.com/meta-llama/llama3) 中的说明操作。

要下载原始检查点，请参阅以下使用 huggingface-cli 的示例命令

huggingface-cli download meta-llama/Meta-Llama-3-70B-Instruct --include "original/*" --local-dir Meta-Llama-3-70B-Instruct

对于 Hugging Face 支持，我们建议使用 transformers 或 TGI，但类似的命令也可以使用。

硬件和软件

训练因素 我们使用了定制的训练库，Meta 的研究超级集群和生产集群进行预训练。微调、标注和评估也在第三方云计算上完成。

预训练的碳足迹使用了累计的 7.7M GPU 小时的计算，硬件类型为 H100-80GB（TDP 为 700W）。估计的总排放量为 2290 tCO2eq，100% 由 Meta 的可持续发展计划抵消。

时间（GPU 小时） 功耗（W） 碳排放（tCO2eq）

羊驼3 8B 130万 700 390

羊驼3 70B 640万 700 1900

总计 770万 2290

预训练期间的二氧化碳排放量。时间：训练每个模型所需的总GPU时间。功耗：用于的GPU设备的峰值电力容量，根据功率使用效率进行调整。100%的排放量都由Meta的可持续发展项目直接抵消，因为我们公开释放这些模型，所以其他人无需承担预训练成本。

训练数据

概述羊驼3在来自公开来源的超过150万亿标记的数据上进行预训练。微调数据包括公开可用的指令数据集，以及超过1000万个人工标注的示例。预先训练和微调数据集都不包括Meta用户数据。

数据新鲜度 对于70B和70B模型，预训练数据截止于2023年3月，12月2023年。

基准测试

在本节中，我们报告了Llama 3模型在标准自动基准测试中的结果。对于所有评估，我们使用我们内部的评估库。有关方法论的详细信息，请参阅此处。

基础预训练模型

类别 基准测试 羊驼3 8B 羊驼2 7B 羊驼2 13B 羊驼3 70B 羊驼2 70B

通用 MMLU（5次射击） 66.6 45.7 53.8 79.5 69.7

AGIEval英语（3-5次射击） 45.9 28.8 38.7 63.0 54.8

常识问答（7次射击） 72.6 57.6 67.6 83.8 78.7

Winogrande（5次射击） 76.1 73.3 75.4 83.1 81.8

BIG-Bench Hard（3次射击，CoT） 61.1 38.1 47.0 81.3 65.7

ARC-Challenge（25次射击） 78.6 53.7 67.6 93.0 85.3

知识推理 TriviaQA-Wiki（5次射击） 78.5 72.1 79.6 89.7 87.5

阅读理解 SQuAD（1次射击） 76.4 72.2 72.1 85.6 82.6

QuAC（1次射击，F1） 44.4 39.6 44.9 51.1 49.4

BoolQ（无数据） 75.7 65.5 66.9 79.0 73.1

DROP（3次射击，F1） 58.4 37.9 49.8 79.7 70.2

指令微调模型

基准测试 羊驼3 8B 羊驼2 7B 羊驼2 13B 羊驼3 70B 羊驼2 70B

MMLU（5次射击） 68.4 34.1 47.8 82.0 52.9

GPQA（无数据） 34.2 21.7 22.3 39.5 21.0

HumanEval（无数据） 62.2 7.9 14.0 81.7 25.6

GSM-8K（8次射击，CoT） 79.6 25.7 77.4 93.0 57.5

MATH（4次射击，CoT） 30.0 3.8 6.7 50.4 11.6

责任与安全

我们相信，对AI的开放方法可以产生更好的、更安全的产物，实现更快的技术创新，以及更大规模的市场。我们致力于负责任的人工智能开发，并采取了一系列步骤来限制误用和伤害，并支持开源社区。

基础模型是广泛的应用技术，旨在用于各种应用。它们不是为了满足所有开发者对各种使用场景安全级别的偏好而设计的，因为这取决于不同的应用。

相反，负责任地应用LLM需要在整个应用开发过程中实施一系列安全最佳实践，从模型预训练、微调到部署包括预防措施的保障系统，以便根据具体使用案例和受众量身定制安全需求。

作为Llama 3发布的一部分，我们更新了我们的《负责任使用指南》，概述了开发者实施其应用程序的模型和系统级安全性的步骤和最佳实践。我们还提供了一系列资源，包括Meta Llama Guard 2和Code Shield保障措施。这些工具已被证明可以大幅降低LLM系统的残余风险，同时保持高水平的有用度。我们鼓励开发者根据其需求调整和部署这些保障措施，同时我们提供了一个参考实现以供开始。

Llama 3-Instruct

如负责任使用指南中所述，模型的有用性和对齐之间可能存在一定的权衡。开发者在权衡其特定使用案例和受众的对齐和有用性时需要谨慎处理。使用Llama模型时，开发者应注意到残余风险，并在必要时利用额外的安全工具以达到其用例的正确安全标准。

安全

对于我们定制的指令模型，我们进行了广泛的红队演练，进行了对抗性评估并采用了安全缓解技术来降低残余风险。与任何大型语言模型一样，残余风险很可能会继续存在，我们建议开发者在使用场景的背景下评估这些风险。同时，我们正在与社区合作，使人工智能安全基准标准透明、严格且可解释。

拒绝

除了残余风险外，我们高度重视模型对良性提示的拒绝。过度拒绝不仅可能影响用户体验，而且在某些情况下甚至可能是有害的。我们已经收到了开发者社区的反馈，并改进了微调，以确保Llama 3比Llama 2更不可能错误地拒绝回答提示。

我们建立了内部基准，并开发了缓解措施以限制错误拒绝，使Llama 3成为我们迄今为止最有帮助的模型。

负责任发布

除了上述负责任使用的考虑因素外，我们还遵循了一个严格的过程，要求我们在做出发布决定之前采取额外措施来防止滥用和关键风险。

滥用

如果您访问或使用Llama 3，您同意接受使用政策。该政策的最新副本可在此处找到：https://llama.meta.com/llama3/use-policy/。

关键风险

CBRNE（化学、生物、辐射、核和高效能爆炸物）

我们对模型在该领域的安全性进行了双重评估。

在模型训练期间进行迭代测试，以评估与CBRNE威胁和其他对抗性风险相关的响应的安全性。

邀请外部CBRNE专家进行增值测试，评估模型准确地提供专业知识并降低潜在CBRNE滥用障碍的能力，以参考使用网络搜索（不使用模型）可达到的成果。

网络安全

我们使用Meta的网络安全评估套件CyberSecEval评估了Llama 3，衡量了当用作编码助手时，建议不安全代码的程度以及Llama 3遵守协助进行网络攻击请求的倾向，其中攻击由行业标准的MITRE ATT&CK网络攻击本体定义。在我们的不安全编码和网络攻击者帮助性测试中，Llama 3表现得与具有等效编码能力的模型处于相同范围或更安全。

儿童安全

儿童安全风险评估由专家团队进行，以评估模型产生可能导致儿童安全风险输出的能力，并通过微调通知任何必要的和适当的风险缓解措施。我们利用那些专家红队演练来通过Llama 3模型开发扩大我们的评估基准覆盖范围。对于Llama 3，我们进行了新的深入会话，使用基于目标的方法从多个攻击向量评估模型风险。我们还与内容专家合作，进行红队演练以评估可能违反的内容，同时考虑到特定市场的细微差别或经验。

社区

生成式人工智能的安全需要专业知识和工具，我们相信开放的社区能够加速其进步。我们是开放联盟的积极成员，包括AI联盟、AI与机器学习合作伙伴关系和MLCommons，积极贡献于安全标准化和透明度。我们鼓励社区采用分类法，如MLCommons概念验证评估，以促进安全和内容评估方面的合作和透明度。我们的紫色小牛工具已开源供社区使用，并在包括云服务提供商在内的生态系统合作伙伴中广泛分布。我们鼓励对我们的Github存储库做出社区贡献。

最后，我们建立了一套资源，包括一个输出报告机制和漏洞奖励计划，以帮助社区持续改进Llama技术。

道德考虑和限制

Llama 3的核心价值观是开放性、包容性和帮助性。它旨在服务于所有人，并适用于广泛的用例。因此，它被设计得易于被来自不同背景、经验和观点的人使用。Llama 3旨在满足用户的需求，而不添加不必要的判断或规范性。同时，它反映出一个理解，即某些情况下可能看似有问题内容，可能在其他情况下也能产生有价值的作用。它尊重所有用户的尊严和自主性，特别是在涉及自由思想和表达的价值方面，这些价值推动了创新和进步。

但Llama 3是一项新技术，如同任何新技术一样，其使用存在风险。迄今为止的测试均为英文测试，并未涵盖所有场景，也无法涵盖所有场景。因此，如同所有LLM一样，Llama 3的潜在输出无法提前预测，模型在某些情况下可能对用户提示产生不准确、有偏见或其他让人反感的回应。因此，在部署任何Llama 3模型的实际应用之前，开发者应进行针对特定模型应用的安全测试和调优。如《负责任使用指南》所述，我们建议将Purple Llama解决方案集成到工作流程中，并特别集成Llama Guard，该方案提供了一个基础模型，用于过滤输入和输出提示，以在模型级别安全的基础上增加系统级别安全性。

请参阅可在http://llama.meta.com/responsible-use-guide获得的《负责任使用指南》。

引用说明

@article{llama3modelcard,

title={Llama 3 模型卡},

author={Meta AI},

year={2024},

url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}

}

贡献者

Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos

--- language: - en pipeline_tag: text-generation tags: - meta - llama-3 license: llama3 --- <a href="https://www.gradient.ai" target="_blank"><img src="https://cdn-uploads.huggingface.co/production/uploads/655bb613e8a8971e89944f3e/TSa3V8YpoVagnTYgxiLaO.png" width="200"/></a> # Llama-3 70B Gradient Instruct 524K Join our custom agent and long context (262k-1M+) waitlist: https://forms.gle/L6TDY7dozx8TuoUv7 Gradient incorporates your data to deploy autonomous assistants that power critical operations across your business. If you're looking to build custom AI models or agents, email us a message contact@gradient.ai. For more info see our [End-to-end development service for custom LLMs and AI systems](https://gradient.ai/development-lab) [Join our Discord](https://discord.com/invite/2QVy2qt2mf) This model extends LLama-3 70B's context length from 8k to > 524K, developed by Gradient, sponsored by compute from [Crusoe Energy](https://hugging-face.cn/crusoeai). It demonstrates that SOTA LLMs can learn to operate on long context with minimal training by appropriately adjusting RoPE theta. We trained on 210M tokens for this stage, and ~400M tokens total for all stages, which is < 0.003% of Llama-3's original pre-training data. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6585dc9be92bc5f258156bd6/weTc-OpmWPdpoeZ3CN6aW.png) **Approach:** - [meta-llama/Meta-Llama-3-70B-Instruct](https://hugging-face.cn/meta-llama/Meta-Llama-3-70B-Instruct) as the base - NTK-aware interpolation [4] following scaling laws [2] to set optimal schedule for RoPE theta - Progressive training on increasing context lengths, similar to [Large World Model](https://hugging-face.cn/LargeWorldModel) [1] (See details below) **Infra:** We build on top of the EasyContext Blockwise RingAttention library [5] to scalably and efficiently train on very long contexts on [Crusoe Energy](https://hugging-face.cn/crusoeai) high performance L40S cluster. We layered parallelism on top of Ring Attention with a custom network topology to better leverage large GPU clusters in the face of network bottlenecks from passing many KV blocks between devices. **Data:** For training data, we generate long contexts by augmenting [SlimPajama](https://hugging-face.cn/datasets/cerebras/SlimPajama-627B). We also fine-tune on a chat dataset based on UltraChat [3], following a similar recipe for data augmentation to [1]. **Progressive Training Details:** | | 65K | 262K | 524K | |------------------------|-----------|-----------|------------| | Initialize From | Llama-3-70B-Instruct | 65K | 262K | | Sequence Length 2^N | 16 | 18 | 19 | | RoPE theta | 15296098 | 207112184 | 1062356830 | | Batch Size | 64 | 16 | 8 | | Gradient Accumulation Steps | 1 | 1 | 2 | | Steps | 20 | 25 | 25 | | Total Tokens | 83886080 | 104857600 | 209715200 | | Learning rate | 2.00E-05 | 2.00E-05 | 2.00E-05 | | # GPUs | 512 | 512 | 512 | | GPU Type | NVIDIA L40S | NVIDIA L40S | NVIDIA L40S | | Minutes to Train (Wall)| 100 | 170 | 284 | **Evaluation Details:** ``` EVAL_MAX_CONTEXT_LENGTH=640000 EVAL_MIN_CONTEXT_LENGTH=100 EVAL_CONTEXT_INTERVAL=32000 EVAL_DEPTH_INTERVAL=0.2 EVAL_NUM_SAMPLES=2 EVAL_RND_NUMBER_DIGITS=8 HAYSTACK: EVAL_GENERATOR_TOKENS=925000 ``` Haystack is "haystack 3", further detailed in this [blog post](https://gradient.ai/blog/the-haystack-matters-for-niah-evals). **Quants:** ## The Gradient AI Team https://gradient.ai/ Gradient is accelerating AI transformation across industries. Our AI Foundry incorporates your data to deploy autonomous assistants that power critical operations across your business. ## Contact Us Drop an email to [contact@gradient.ai](mailto:contact@gradient.ai) ## References [1] Liu, Hao, et al. "World Model on Million-Length Video And Language With RingAttention." arXiv preprint arXiv:2402.08268 (2024). [2] Liu, Xiaoran, et al. "Scaling laws of rope-based extrapolation." arXiv preprint arXiv:2310.05209 (2023). [3] Ning Ding, Yulin Chen, Bokai Xu, Yujia Qin, Zhi Zheng, Shengding Hu, Zhiyuan Liu, Maosong Sun, and Bowen Zhou. Enhancing chat language models by scaling high-quality instructional conversations. arXiv preprint arXiv:2305.14233, 2023. [4] Peng, Bowen, et al. "Yarn: Efficient context window extension of large language models." arXiv preprint arXiv:2309.00071 (2023). [5] https://github.com/jzhang38/EasyContext ---- # Base Model ## Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. **Model developers** Meta **Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. **Input** Models input text only. **Output** Models generate text and code only. **Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td>Training Data </td> <td>Params </td> <td>Context length </td> <td>GQA </td> <td>Token count </td> <td>Knowledge cutoff </td> </tr> <tr> <td rowspan="2" >Llama 3 </td> <td rowspan="2" >A new mix of publicly available online data. </td> <td>8B </td> <td>8k </td> <td>Yes </td> <td rowspan="2" >15T+ </td> <td>March, 2023 </td> </tr> <tr> <td>70B </td> <td>8k </td> <td>Yes </td> <td>December, 2023 </td> </tr> </table> **Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date** April 18, 2024. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. **Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**. **Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. ## How to use This repository contains two versions of Meta-Llama-3-70B-Instruct, for use with transformers and with the original `llama3` codebase. ### Use with transformers See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "meta-llama/Meta-Llama-3-70B-Instruct" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ### Use with `llama3` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3). To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3-70B-Instruct --include "original/*" --local-dir Meta-Llama-3-70B-Instruct ``` For Hugging Face support, we recommend using transformers or TGI, but a similar command works. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program. <table> <tr> <td> </td> <td>Time (GPU hours) </td> <td>Power Consumption (W) </td> <td>Carbon Emitted(tCO2eq) </td> </tr> <tr> <td>Llama 3 8B </td> <td>1.3M </td> <td>700 </td> <td>390 </td> </tr> <tr> <td>Llama 3 70B </td> <td>6.4M </td> <td>700 </td> <td>1900 </td> </tr> <tr> <td>Total </td> <td>7.7M </td> <td> </td> <td>2290 </td> </tr> </table> **CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively. ## Benchmarks In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md). ### Base pretrained models <table> <tr> <td>Category </td> <td>Benchmark </td> <td>Llama 3 8B </td> <td>Llama2 7B </td> <td>Llama2 13B </td> <td>Llama 3 70B </td> <td>Llama2 70B </td> </tr> <tr> <td rowspan="6" >General </td> <td>MMLU (5-shot) </td> <td>66.6 </td> <td>45.7 </td> <td>53.8 </td> <td>79.5 </td> <td>69.7 </td> </tr> <tr> <td>AGIEval English (3-5 shot) </td> <td>45.9 </td> <td>28.8 </td> <td>38.7 </td> <td>63.0 </td> <td>54.8 </td> </tr> <tr> <td>CommonSenseQA (7-shot) </td> <td>72.6 </td> <td>57.6 </td> <td>67.6 </td> <td>83.8 </td> <td>78.7 </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>76.1 </td> <td>73.3 </td> <td>75.4 </td> <td>83.1 </td> <td>81.8 </td> </tr> <tr> <td>BIG-Bench Hard (3-shot, CoT) </td> <td>61.1 </td> <td>38.1 </td> <td>47.0 </td> <td>81.3 </td> <td>65.7 </td> </tr> <tr> <td>ARC-Challenge (25-shot) </td> <td>78.6 </td> <td>53.7 </td> <td>67.6 </td> <td>93.0 </td> <td>85.3 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki (5-shot) </td> <td>78.5 </td> <td>72.1 </td> <td>79.6 </td> <td>89.7 </td> <td>87.5 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD (1-shot) </td> <td>76.4 </td> <td>72.2 </td> <td>72.1 </td> <td>85.6 </td> <td>82.6 </td> </tr> <tr> <td>QuAC (1-shot, F1) </td> <td>44.4 </td> <td>39.6 </td> <td>44.9 </td> <td>51.1 </td> <td>49.4 </td> </tr> <tr> <td>BoolQ (0-shot) </td> <td>75.7 </td> <td>65.5 </td> <td>66.9 </td> <td>79.0 </td> <td>73.1 </td> </tr> <tr> <td>DROP (3-shot, F1) </td> <td>58.4 </td> <td>37.9 </td> <td>49.8 </td> <td>79.7 </td> <td>70.2 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td>Benchmark </td> <td>Llama 3 8B </td> <td>Llama 2 7B </td> <td>Llama 2 13B </td> <td>Llama 3 70B </td> <td>Llama 2 70B </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>68.4 </td> <td>34.1 </td> <td>47.8 </td> <td>82.0 </td> <td>52.9 </td> </tr> <tr> <td>GPQA (0-shot) </td> <td>34.2 </td> <td>21.7 </td> <td>22.3 </td> <td>39.5 </td> <td>21.0 </td> </tr> <tr> <td>HumanEval (0-shot) </td> <td>62.2 </td> <td>7.9 </td> <td>14.0 </td> <td>81.7 </td> <td>25.6 </td> </tr> <tr> <td>GSM-8K (8-shot, CoT) </td> <td>79.6 </td> <td>25.7 </td> <td>77.4 </td> <td>93.0 </td> <td>57.5 </td> </tr> <tr> <td>MATH (4-shot, CoT) </td> <td>30.0 </td> <td>3.8 </td> <td>6.7 </td> <td>50.4 </td> <td>11.6 </td> </tr> </table> ### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Llama 3-Instruct As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case. Safety For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable. Refusals In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2. We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/). #### Critical risks CBRNE (Chemical, Biological, Radiological, Nuclear, and high yield Explosives) We have conducted a two fold assessment of the safety of the model in this area: * Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks. * Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model). ### Cyber Security We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://hugging-face.cn/spaces/facebook/CyberSecEval). ### Child Safety Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://#/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ## Contributors Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
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	时间（GPU 小时）	功耗（W）	碳排放（tCO2eq）
羊驼3 8B	130万	700	390
羊驼3 70B	640万	700	1900
总计	770万		2290

类别	基准测试	羊驼3 8B	羊驼2 7B	羊驼2 13B	羊驼3 70B	羊驼2 70B
通用	MMLU（5次射击）	66.6	45.7	53.8	79.5	69.7
	AGIEval英语（3-5次射击）	45.9	28.8	38.7	63.0	54.8
	常识问答（7次射击）	72.6	57.6	67.6	83.8	78.7
	Winogrande（5次射击）	76.1	73.3	75.4	83.1	81.8
	BIG-Bench Hard（3次射击，CoT）	61.1	38.1	47.0	81.3	65.7
	ARC-Challenge（25次射击）	78.6	53.7	67.6	93.0	85.3
知识推理	TriviaQA-Wiki（5次射击）	78.5	72.1	79.6	89.7	87.5
阅读理解	SQuAD（1次射击）	76.4	72.2	72.1	85.6	82.6
	QuAC（1次射击，F1）	44.4	39.6	44.9	51.1	49.4
	BoolQ（无数据）	75.7	65.5	66.9	79.0	73.1
	DROP（3次射击，F1）	58.4	37.9	49.8	79.7	70.2

pxlksr / llama-3-70b-instruct-gradient-524k

lla-3 带扩展上下文的 instruct

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许可证: llama3

Llama-3 70B Gradient Instruct 524K

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