Command R+模型介绍

目录

Model Summary  模型概要

Evaluations  评价

Tool use & multihop capabilities:工具使用和多跳功能：

Grounded Generation and RAG Capabilities:接地发电和 RAG 功能：

Code Capabilities:  代码能力：

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参考网址：

https://huggingface.co/CohereForAI/c4ai-command-r-plus

Model Summary  模型概要

C4AI Command R+ is an open weights research release of a 104B billion parameter model with highly advanced capabilities, this includes Retrieval Augmented Generation (RAG) and tool use to automate sophisticated tasks. The tool use in this model generation enables multi-step tool use which allows the model to combine multiple tools over multiple steps to accomplish difficult tasks. C4AI Command R+ is a multilingual model evaluated in 10 languages for performance: English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Arabic, and Simplified Chinese. Command R+ is optimized for a variety of use cases including reasoning, summarization, and question answering.
C4AI Command R+ 是一个 104B 十亿参数模型的开放权重研究版本，具有高度先进的功能，其中包括检索增强生成 (RAG) 和用于自动执行复杂任务的工具。此模型生成中使用的工具支持多步骤工具使用，这允许模型在多个步骤中组合多个工具来完成困难的任务。 C4AI Command R+ 是一个多语言模型，以 10 种语言进行性能评估：英语、法语、西班牙语、意大利语、德语、巴西葡萄牙语、日语、韩语、阿拉伯语和简体中文。 Command R+ 针对各种用例进行了优化，包括推理、总结和问答。

C4AI Command R+ is part of a family of open weight releases from Cohere For AI and Cohere. Our smaller companion model is C4AI Command R
C4AI Command R+ 是 Cohere For AI 和 Cohere 开放权重版本系列的一部分。我们较小的配套模型是 C4AI Command R

Model Architecture: This is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model uses supervised fine-tuning (SFT) and preference training to align model behavior to human preferences for helpfulness and safety.
模型架构：这是一种使用优化的转换器架构的自回归语言模型。预训练后，该模型使用监督微调 (SFT) 和偏好训练来使模型行为与人类偏好保持一致，以实现有用性和安全性。

Languages covered: The model is optimized to perform well in the following languages: English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Simplified Chinese, and Arabic.
涵盖的语言：该模型经过优化，可以在以下语言中表现良好：英语、法语、西班牙语、意大利语、德语、巴西葡萄牙语、日语、韩语、简体中文和阿拉伯语。

Pre-training data additionally included the following 13 languages: Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, Persian.
预训练数据还包括以下 13 种语言：俄语、波兰语、土耳其语、越南语、荷兰语、捷克语、印度尼西亚语、乌克兰语、罗马尼亚语、希腊语、印地语、希伯来语、波斯语。

Context length: Command R+ supports a context length of 128K.
上下文长度：命令 R+ 支持 128K 的上下文长度。

Evaluations  评价

Command R+ has been submitted to the Open LLM leaderboard. We include the results below, along with a direct comparison to the strongest state-of-art open weights models currently available on Hugging Face. We note that these results are only useful to compare when evaluations are implemented for all models in a standardized way using publically available code, and hence shouldn't be used for comparison outside of models submitted to the leaderboard or compared to self-reported numbers which can't be replicated in the same way.
命令 R+ 已提交至 Open LLM 排行榜。我们提供了下面的结果，以及与 Hugging Face 目前可用的最先进的开放权重模型的直接比较。我们注意到，这些结果仅在使用公开代码以标准化方式对所有模型实施评估时才有用，因此不应用于提交到排行榜的模型之外的比较或与自我报告的数字进行比较。无法以同样的方式复制。

We include these metrics here because they are frequently requested, but note that these metrics do not capture RAG, multilingual, tooling performance or the evaluation of open ended generations which we believe Command R+ to be state-of-art at. For evaluations of RAG, multilingual and tooling read more here. For evaluation of open ended generation, Command R+ is currently being evaluated on the chatbot arena.
我们在此处包含这些指标是因为它们经常被要求，但请注意，这些指标并未捕获 RAG、多语言、工具性能或开放式生成的评估，我们认为 Command R+ 在这些方面是最先进的。有关 RAG、多语言和工具的评估，请在此处阅读更多内容。为了评估开放式生成，Command R+ 目前正在聊天机器人领域进行评估。

Tool use & multihop capabilities:
工具使用和多跳功能：

Command R+ has been specifically trained with conversational tool use capabilities. These have been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template will likely reduce performance, but we encourage experimentation.
Command R+ 经过专门培训，具备对话工具使用能力。这些已使用特定的提示模板，通过监督微调和偏好微调的混合方式训练到模型中。偏离此提示模板可能会降低性能，但我们鼓励尝试。

Command R+’s tool use functionality takes a conversation as input (with an optional user-system preamble), along with a list of available tools. The model will then generate a json-formatted list of actions to execute on a subset of those tools. Command R+ may use one of its supplied tools more than once.
Command R+ 的工具使用功能将对话作为输入（带有可选的用户系统前导码）以及可用工具列表。然后，该模型将生成一个 json 格式的操作列表，以在这些工具的子集上执行。 Command R+ 可以多次使用其提供的工具之一。

The model has been trained to recognise a special directly_answer tool, which it uses to indicate that it doesn’t want to use any of its other tools. The ability to abstain from calling a specific tool can be useful in a range of situations, such as greeting a user, or asking clarifying questions. We recommend including the directly_answer tool, but it can be removed or renamed if required.
该模型经过训练可以识别特殊的 directly_answer 工具，该工具用于表明它不想使用任何其他工具。不调用特定工具的能力在多种情况下都很有用，例如问候用户或询问澄清问题。我们建议包含 directly_answer 工具，但如果需要，可以将其删除或重命名。

Grounded Generation and RAG Capabilities:
接地发电和 RAG 功能：

Command R+ has been specifically trained with grounded generation capabilities. This means that it can generate responses based on a list of supplied document snippets, and it will include grounding spans (citations) in its response indicating the source of the information. This can be used to enable behaviors such as grounded summarization and the final step of Retrieval Augmented Generation (RAG). This behavior has been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template may reduce performance, but we encourage experimentation.
Command R+ 经过专门培训，具备接​​地发电能力。这意味着它可以根据提供的文档片段列表生成响应，并且它将在响应中包含指示信息来源的基础跨度（引用）。这可用于启用诸如扎根总结和检索增强生成 (RAG) 的最后一步等行为。这种行为已使用特定的提示模板，通过监督微调和偏好微调的混合方式训练到模型中。偏离此提示模板可能会降低性能，但我们鼓励尝试。

Command R+’s grounded generation behavior takes a conversation as input (with an optional user-supplied system preamble, indicating task, context and desired output style), along with a list of retrieved document snippets. The document snippets should be chunks, rather than long documents, typically around 100-400 words per chunk. Document snippets consist of key-value pairs. The keys should be short descriptive strings, the values can be text or semi-structured.
Command R+ 的基础生成行为将对话作为输入（带有可选的用户提供的系统前导码，指示任务、上下文和所需的输出样式），以及检索到的文档片段列表。文档片段应该是块，而不是长文档，通常每个块大约有 100-400 个单词。文档片段由键值对组成。键应该是简短的描述性字符串，值可以是文本或半结构化的。

By default, Command R+ will generate grounded responses by first predicting which documents are relevant, then predicting which ones it will cite, then generating an answer. Finally, it will then insert grounding spans into the answer. See below for an example. This is referred to as accurate grounded generation.
默认情况下，Command R+ 将首先预测哪些文档相关，然后预测它将引用哪些文档，然后生成答案，从而生成接地响应。最后，它会将接地跨度插入到答案中。请参阅下面的示例。这被称为 accurate 接地一代。

The model is trained with a number of other answering modes, which can be selected by prompt changes. A fast citation mode is supported in the tokenizer, which will directly generate an answer with grounding spans in it, without first writing the answer out in full. This sacrifices some grounding accuracy in favor of generating fewer tokens.
该模型使用多种其他回答模式进行训练，可以通过提示更改来选择。分词器支持 fast 引用模式，它将直接生成包含基础跨度的答案，而无需先完整写出答案。这牺牲了一些接地精度，有利于生成更少的令牌。

Code Capabilities:  代码能力：

Command R+ has been optimized to interact with your code, by requesting code snippets, code explanations, or code rewrites. It might not perform well out-of-the-box for pure code completion. For better performance, we also recommend using a low temperature (and even greedy decoding) for code-generation related instructions.
Command R+ 已经过优化，可以通过请求代码片段、代码解释或代码重写来与您的代码进行交互。对于纯代码补全来说，它可能无法很好地开箱即用。为了获得更好的性能，我们还建议对代码生成相关指令使用低温（甚至贪婪解码）。

完结。