{ "source_file": "0930", "source_platform": "个人博客", "author": "Sebastian Ruder", "title_listed": "Command R+", "keywords": "Command R,RAG,开源模型", "domain": "大模型算法", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://www.ruder.io/command-r/", "publish_date": "2024-04", "corpus_stage": "推理部署", "notes": "Cohere Command R+ 模型解析", "id": "ff6e28f092a4", "collected_at": "2026-10-02T14:15:43", "title": "Command R+", "body_markdown": "rag\nCommand R+\nThis post discusses Command R and Command R+, the top open-weights model on Chatbot Arena at the time of its release and highlights their RAG and multilingual capabilities.\nSebastian Ruder\n15 Apr 2024\n•\n7 min read\nShare\nChatbot Arena leaderboard as of April 9, 2024 (source:\nlmsys.org\n).\nUpdate August 30, 2024\n: We've released\nupdated versions of Command R and R+\n.\nThis post is an update on what I’ve been up to since I joined Cohere. I’ve had fun contributing to the launch of\nCommand R\nand\nR+\n, the latest Cohere models. Command R+ is ranked as the top open-weights model on\nChatbot Arena\n, even outperforming some versions of GPT-4. Why is this exciting?\nThis post was first published in\nNLP News\n.\nChatbot Arena\nLet’s talk about why we should care about Chatbot Arena rankings in the first place. I’ve written in the past about\nchallenges in NLP benchmarking\n. Pre-LLM benchmarks such as\nSuperGLUE\nmostly consist of classification tasks and no longer provide sufficient signal to differentiate the latest generation of LLMs. More recent benchmarks such as\nMT-Bench\nconsist of small samples of open-ended questions and rely on LLMs as evaluators, which have their\nown sets\nof biases\n.\nMMLU\n, one of the most widely used benchmarks consisting of 14k multiple-choice questions sourced from public sources covering 57 domains has been featured prominently in\nGPT-4\n,\nClaude 3\n, and\nMistral Large\nposts. The data is\nnot without errors\n, however, and given its release in 2020, training data of recent models is likely at\nleast partially contaminated\n.\nChatbot Arena\nis a platform where users rate conversations in a blind A/B test. They can continue the conversation until they choose a winner. Of course, short user interactions often do not reveal more advanced model capabilities and annotators can be\nfooled by authoritative but non-factual answers\n. Nevertheless, this is the closest to an assessment on realistic user interactions that we currently have. As models are always evaluated based on new user conversations, there is no risk of data contamination.\nCommand R+ outperforms versions of GPT-4 on Chatbot Arena while being much cheaper to use. It does also well on use cases that are under-represented in Chatbot Arena such as RAG, tool use, and multilinguality.\n(left) Performance comparison of Command R+, Mistral-Large, and GPT4-turbo on three key capabilities: Multilingual, RAG, and Tool Use. (right) Comparison input and output token costs per million for models available on Azure.\nSource\nA GPT-4 Level Model on Your Computer\nCommand R+ consists of 104B parameters with\npublicly available weights\n. This is the first time that a model that is close to GPT-4 performance is available for research use. With the right setup, Command R+ can generate text at a\nrate of 111 tokens/s\n(!) when deployed locally. To understand how to effectively prompt the model, check out the\nprompting guide\n.\nI’m excited about what this means for the open-source community and research, with the gap between closed-source and open-weight models closing and SOTA-level conversational models being more easily accessible.\nThe gap between closed-source and open-weights models on Chatbot Arena is closing (source:\nMaxime Labonne\n).\nOther recently released models such as\nDBRX\n(132B parameters),\nMixtral 8x22B\n(176B parameters), and\nGrok-1\n(314B parameters) are based on a Mixture-of-Experts (MoE), trading off inference speed for memory costs. While these models only activate a subset of parameters for each token, they still require storing all parameters in-memory, which makes them harder to use locally. So far, they are not available or rank much lower than Command R+ on\nChatbot Arena\n.\nCommand R+ comes with a non-commercial license. If you want to self-host or fine-tune it for commercial purposes,\nwe’ll work with you\nto find something that works for you.\nRAG and Tool Use\nWhile Command R+ can be used as a chatbot, it has been designed for enterprise use. Faithful and verifiable responses are especially important in an enterprise setting. Reducing hallucinations and providing trustworthy responses are important research challenges. There are different ways to mitigate hallucinations, ranging from debiasing and model editing to specialized decoding strategies (see\nHuang et al. (2023)\nfor an overview).\nRetrieval-augmented generation (RAG;\nLewis et al., 2020\n), which conditions on the LLM’s generation on retrieved documents is the most practical paradigm IMO. Command R+ uses RAG with in-line citations to provide grounded responses.\nHowever, evaluation of the quality and trustworthiness of such responses is challenging and motivated the development of new evaluation frameworks such as Attributable to Identified Sources (AIS;\nRashkin et al., 2023\n). On our internal human evaluation measuring citation fidelity, Command R+ outperforms GPT4-turbo. On public multi-hop QA benchmarks, it outperforms models at the same price point such as Claude 3 Sonnet and Mistral-large.\n(left) Human head-to-head preference results using a holistic grading scheme combining text fluency, citation quality, and overall utility. (right) Accuracy of multi-hop REACT agents powered by various models with access to the same search tools retrieving from Wikipedia (HotpotQA) and the Internet (Bamboogle and StrategyQA).\nSource\nYou can easily use RAG via the\nAPI\non the Internet or your own documents. A complete RAG workflow involves document search and reranking, which can be seen in\nthis Colab\nwith an example RAG setup on Wikipedia.\nExample of basic RAG usage with Command models using the Cohere API (\nsource\n).\nIn enterprise settings, seamless integrations with existing APIs and services is crucial. I’ve\nwritten before\nabout the promise of tool-augmented models. Tools can help decompose complex problems and make LLMs outputs more interpretable by enabling users to look at the trace of API calls. Command R+ has been trained for zero-shot multi-step tool use. On public tool use benchmarks, it outperforms GPT4-turbo.\nConversational tool-use and single-turn function-calling evaluations using Microsoft’s ToolTalk (Hard) benchmark (\nFarn & Shin 2023\n) and Berkeley's Function Calling Leaderboard (BFCL) (\nYan et al. 2024\n).\nSource\nThe recommended way to leverage multi-step tool use with Command R+ is via\nLangChain\n. To teach the model to use a new tool, you only need to provide the name, definition (a Python function), and the arguments schema. The model can then be used as a ReAct agent in LangChain with a range of tools (see\nthis Colab\nfor an example workflow).\nExample of using Command R+ in LangChain with different tools including Internet search, vector store search, and Python execution.\nI hope that strong support for RAG and tool use in an open-weights model will lead to progress in important research directions, some of which I have outlined\nhere\n. If you want the most efficient solution for RAG,\nCommand R\ndemonstrates highly competitive RAG and tool-use performance at cheaper cost (35B-parameter weights are\npublicly available\n).\nMultilingual\nCommand R+ works well in languages beyond English. It was\npre-trained on 23 languages\n, with our main focus on 10 key language of global business: English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Simplified Chinese, and Arabic. In our evaluations on translation tasks, it is competitive with GPT4-turbo.\nComparison of models on FLoRES (in French, Spanish, Italian, German, Portuguese, Japanese, Korean, Arabic, and Chinese) and WMT23 (in German, Japanese, and Chinese) translation tasks.\nCommand R+ has been designed with multilinguality in mind. Its tokenizer is much less English-centric than others and compresses text in non-English languages much better than both the Mistral and OpenAI tokenizers. As LLM providers charge based on the number of input/output tokens, tokenizer choice directly impacts API costs for users. At the same cost-per-token, if one LLM generates 2x as many tokens as another, the API costs will also be twice as large.\nComparison of the number of tokens produced by the Cohere, Mistral (Mixtral), and OpenAI tokenizers for different languages (as a multiple of the number of tokens produced by the Cohere tokenizer). The Cohere tokenizer produces much fewer tokens to represent the same text, with particularly large reductions on non-Latin script languages. For instance, in Japanese, the OpenAI tokenizer outputs 1.67x as many tokens as the Cohere tokenizer.\nAhia et al. (2023)\nhighlighted that such over-segmentation leads to “double unfairness”: higher API prices and lower utility (reduced performance) for many languages. In comparison, Command R+ is much more equitable. I hope that companies will take into account the impact of tokenization and other design choices on API costs in future LLMs.\nGiven the focus on the Latin script in existing models, I particularly want to highlight Command R+’s performance in some prominent non-Latin script languages: Japanese, Korean, and Chinese. We evaluated on translation tasks as well as language-specific benchmarks such as\nJapanese MT-Bench\n8\n. Command R+ outperforms Claude 3 Sonnet and Mistral Large and is competitive with GPT 4-Turbo.\nJapanese evaluation on FLoRES, WMT23, and Japanese MT-Bench (\nsource\n).\nWe see similar trends for evaluations in\nKorean\nand\nChinese\n. On\nChinese Chatbot Arena\n, Command R+ is only behind GPT4 and Claude 3 Opus, models that are 2–3x more expensive. It’s been exciting to read the feedback from speakers of different language communities using Command R+.\nConclusion\nOverall, I’m really excited about Command R+’s capabilities and the future of LLMs. We will be pushing its multilingual capabilities to make it useful in many languages used in business. I’ve been using Command R+ with RAG via\nCohere’s playground\nfor my exploratory Internet searches and creative tasks in English and German and have been impressed by its quality. Feel free to try it in your language—and share your feedback in the comments or via email. 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