MOSS-003：预训练模型与插件增强多轮对话数据

文章主题：moss, moon, plugin

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IT之家 4 月 21 日消息，复旦大学自然语言处理实验室开发的新版 MOSS 模型今日正式上线，成为国内首个插件增强的开源对话语言模型。

目前，MOSS 模型已上线开源，相关代码、数据、模型参数已在 Github 和 Hugging Face 等平台开放，供科研人员下载。

据介绍，MOSS 是一个支持中英双语和多种插件的开源对话语言模型，moss-moon 系列模型具有 160 亿参数，在 FP16 精度下可在单张 A100 / A800 或两张 3090 显卡运行，在 INT4/8 精度下可在单张 3090 显卡运行。MOSS 基座语言模型在约七千亿中英文以及代码单词上预训练得到，后续经过对话指令微调、插件增强学习和人类偏好训练具备多轮对话能力及使用多种插件的能力。

MOSS 来自复旦大学自然语言处理实验室的邱锡鹏教授团队，名字与《流浪地球》电影中的 AI 同名，已发布至公开平台（https://moss.fastnlp.top/），邀请公众参与内测。

IT之家查看 MOSS 的 GitHub 页面发现，该项目所含代码采用 Apache 2.0 协议，数据采用 CC BY-NC 4.0 协议，模型权重采用 GNU AGPL 3.0 协议。如需将该项目所含模型用于商业用途或公开部署，需要签署文件并发送至 robot@fudan.edu.cn 取得授权，商用情况仅用于记录，不会收取任何费用。

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moss-moon-003-sft: 基座模型在约 110 万多轮对话数据上微调得到，具有指令遵循能力、多轮对话能力、规避有害请求能力。

🌟 Moss Moon 003 SFT Plugin: 🌟🚀 The base model was fine-tuned on over 110 thousand rounds of dialogue data and approximately 30 thousand plugin enhancements. It also has the ability to use search engines, generate text from images, perform calculations, and solve equations.💡 Moss Moon 003 SFT is built upon the foundation of Moss Moon 003 and adds four additional features: search engines, image-to-text generation, calculator, and equation-solving capabilities.

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🚀 Moss Moon 003 is the final model trained on the moss-moon-003-sft foundation with a preference model, moss-moon-003-pm. It has better factualness and security as well as more stable response quality. The source code will be released soon.

🎉 Moss Moon 0.03 Plugin: A Model Trained on Moss Moon 0.03 SFT Plugin’s Preferring Model Achieves Stronger Intent Understanding and Plugin Usage Capabilities 🌟🚀 Moss Moon 0.03 Plugin is a powerful tool that has been trained using the Preferring Model of Moss Moon 0.03 SFT Plugin. This model has been optimized to provide better understanding of user intent and improved plugin usage capabilities.💡 The training process involved using Moss Moon 0.03 SFT Plugin’s Preferring Model, which allowed for a more accurate representation of user preferences. This resulted in a model that is capable of providing more relevant and useful responses to users.🚀 Moss Moon 0.03 Plugin will soon be released to the public, allowing users to benefit from its advanced capabilities. With its improved intent understanding and plugin usage abilities, this plugin is sure to revolutionize the way users interact with their devices.💡 If you’re interested in learning more about Moss Moon 0.03 Plugin or trying it out for yourself, stay tuned for updates on our website and social media channels.# moss-moon-003-plugin #intent-understanding #plugin-usage #preferring-model #training-process #release-date #public-release #website #social-media #interested #try-out

✨ Moss-002 SFT Data 🌟🚀 The moss-002-sft-data dataset is a comprehensive resource that encompasses three key aspects of conversation quality: usefulness, fidelity, and harmlessness. It includes over 570,000 English conversations generated by text-davinci-003 and an additional 590,000 Chinese conversations.💡 The data is designed to provide insights into the effectiveness of various conversational models in real-world scenarios. By analyzing these conversations, researchers can evaluate the performance of different approaches to natural language processing (NLP) and gain a better understanding of how humans communicate.📝 The dataset includes both English and Chinese conversations, which allows for cross-cultural analysis and comparison. This is particularly important given the increasing global interconnectedness and the need for effective communication across linguistic barriers.💡 The moss-002-sft-data dataset can be used to train and evaluate conversational models, as well as to develop new NLP techniques. It provides a valuable resource for researchers and developers working in the field of natural language processing.📝 If you’re interested in learning more about this dataset or using it in your research, please don’t hesitate to contact us at [email protected]. We’d be happy to provide additional information and support.# Moss-002 SFT Data 🌟✨ The moss-002-sft-data dataset is a comprehensive resource that encompasses three key aspects of conversation quality: usefulness, fidelity, and harmlessness. It includes over 570,000 English conversations generated by text-davinci-003 and an additional 590,000 Chinese conversations.💡 The data is designed to provide insights into the effectiveness of various conversational models in real-world scenarios. By analyzing these conversations, researchers can evaluate the performance of different approaches to natural language processing (NLP) and gain a better understanding of how humans communicate.📝 The dataset includes both English and Chinese conversations, which allows for cross-cultural analysis and comparison. This is particularly important given the increasing global interconnectedness and the need for effective communication across linguistic barriers.💡 The moss-002-sft-data dataset can be used to train and evaluate conversational models, as well as to develop new NLP techniques. It provides a valuable resource for researchers and developers working in the field of natural language processing.📝 If you’re interested in learning more about this dataset or using it in your research, please don’t hesitate to contact us at [email protected]. We’d be happy to provide additional information and support.

moss-003-sft-data: moss-moon-003-sft 所使用的多轮对话数据，基于 MOSS-002 内测阶段采集的约 10 万用户输入数据和 gpt-3.5-turbo 构造而成，相比 moss-002-sft-data，moss-003-sft-data 更加符合真实用户意图分布，包含更细粒度的有用性类别标记、更广泛的无害性数据和更长对话轮数，约含 110 万条对话数据。目前仅开源少量示例数据，完整数据将在近期开源。

moss-003-sft-plugin-data: moss-moon-003-sft-plugin 所使用的插件增强的多轮对话数据，包含支持搜索引擎、文生图、计算器、解方程等四个插件在内的约 30 万条多轮对话数据。目前仅开源少量示例数据，完整数据将在近期开源。

moss-003-pm-data: moss-moon-003-pm 所使用的偏好数据，包含在约 18 万额外对话上下文数据及使用 moss-moon-003-sft 所产生的回复数据上构造得到的偏好对比数据，将在近期开源。

MOSS 的 GitHub 页面：点此查看

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