重磅！微软开源Deep Speed Chat，人人拥有ChatGPT！

AIGC开放社区2023-04-13

用户通过Deep Speed Chat提供的“傻瓜式”操作，能以最短的时间、最高效的成本训练类ChatGPT大语言模型，这标志着一个人手一个ChatGPT的时代要来了。4月12日，微软宣布开源了Deep Speed Chat，帮助用户轻松训练类ChatGPT等大语言模型，使得人人都能拥有自己的ChatGPT！（开源地址：https://github.com/microsoft/DeepSpeed）...

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精彩评论

ljCEO
2023-04-13
ljCEO
看来明天人工智能要涨了
Mediator
2023-04-13
Mediator
国产gpt们又能进步啦，winwinwin[微笑] 中国🐮🍺
迷茫的渔夫
2023-04-13
迷茫的渔夫
学阿里卖算力[捂脸]
BaoZei
2023-04-13
BaoZei
期待[开心]
Tom_8777
2023-04-13
Tom_8777
软硬件包括数据安全，直到最后的最后还是智能终端设备，通过PC还是手机更或是跨时代的产物智能眼镜智能头盔等等
- 愚心:
  孔
- 新韭菜绿油油:
  已阅
- 卧龙先生_:
  好
- 李丞昱含:
  阅
- 未完待续06:
  [微笑]
HunterRen
2023-04-13
HunterRen
微软很聪明，每个人等于成为了微软的免费员工，微软可以轻松把所有人培养的智慧聚合成超级智慧为微软所用，

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Microsoft Open Source Deep Speed Chat, everyone has ChatGPT!","themeId":"","isJumpTheme":false,"ttsUrl":"https://static.tigerbbs.com/b08ed743dcd64a3d084e8094723b0673","symbols_score_info":{"MSFT":0.9},"content_text":"用户通过Deep Speed Chat提供的“傻瓜式”操作，能以最短的时间、最高效的成本训练类ChatGPT大语言模型，这标志着一个人手一个ChatGPT的时代要来了。4月12日，微软宣布开源了Deep Speed Chat，帮助用户轻松训练类ChatGPT等大语言模型，使得人人都能拥有自己的ChatGPT！（开源地址：https://github.com/microsoft/DeepSpeed） 据悉，Deep Speed Chat是基于微软Deep Speed深度学习优化库开发而成，具备训练、强化推理等功能，还使用了RLHF（人工反馈机制的强化学习）技术，可将训练速度提升15倍以上，成本却大幅度降低。例如，一个130亿参数的类ChatGPT模型，只需1.25小时就能完成训练。简单来说，用户通过Deep Speed Chat提供的“傻瓜式”操作，能以最短的时间、最高效的成本训练类ChatGPT大语言模型，这标志着一个人手一个ChatGPT的时代要来了。微软为什么开源Deep Speed ChatChatGPT只用了几个月的时间便席卷全球，成为史上用户增长速度最快的消费级应用，对世界的经济发展、科技研发起到了重要的推动作用。很多企业、个人用户纷纷想参与到这场AI技术变革中，但由于庞大的资源、资金投入只能望而却步。即便开源领域推出了LLaMa、Alpaca、Vicuna、Databricks-Dolly等优秀的类ChatGPT项目，仍缺乏一个支持端到端的基于RLHF的规模化系统，使得训练强大的类ChatGPT模型非常艰难。例如，使用上述开源项目训练一个67亿参数的类ChatGPT模型，通常需要昂贵的多卡至多节点的GPU集群，但这些资源对大多数科学家、小型企业、个人开发者而言难以获取。即便是拥有这样的资源，现有的开源系统的训练效率通常还不到，这些机器所能达到的最大效率的5%。现在，通过Deep Speed Chat解决资源、算法等难题，轻松、高效的训练数千亿参数的最先进的类ChatGPT模型。Deep Speed Chat技术简单介绍ChatGPT模型的训练是基于InstructGPT论文中的RLHF方式，这使得现有深度学习系统在训练类ChatGPT模型时存在种种局限。现在，通过Deep Speed Chat可以突破这些训练瓶颈，达到最佳效果。Deep Speed Chat拥有强化推理、RLHF模块、RLHF系统三大核心功能。简化 ChatGPT 类型模型的训练和强化推理：只需一个脚本即可实现多个训练步骤，包括使用Huggingface 预训练的模型、使用 DeepSpeed-RLHF 系统运行 InstructGPT 训练的所有三个步骤，生成属于自己的类ChatGPT模型。此外，还提供了一个易于使用的推理API，用于在模型训练后测试对话性能。Deep Speed-RLHF 模块：DeepSpeed-RLHF 复刻了InstructGPT 论文中的训练模式，并确保包括：a) 监督微调（SFT），b) 奖励模型微调和 c) 基于人类反馈的强化学习（RLHF）在内的三个步骤与其对应。此外，还提供了数据抽象和混合功能，以支持用户使用多个不同来源的数据源进行训练。DeepSpeed-RLHF系统：将DeepSpeed的训练和推理能力整合到统一的混合引擎（DeepSpeed Hybrid Engine or DeepSpeed-HE）中用于RLHF 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