Today, we’re introducing Command R+: a state-of-the-art RAG-optimized LLM designed to tackle enterprise-grade workloads and speak the languages of global business.
Our R-series model family is now available on Microsoft Azure, and coming soon to additional cloud providers.
Command R+ offers best-in-class advanced Retrieval-Augmented Generation (RAG) capabilities to provide accurate, enterprise-ready solutions with citations to reduce hallucinations.
Command R+ comes with Tool Use capabilities, accessible through our API and @LangChainAI, to seamlessly automate complex business workflows. We also now support Multi-Step Tool Use enabling the execution of complex tasks by combining multiple tools.
Command R+ is designed to serve as many people, organizations, and markets as possible. The model excels at 10 key languages of global business helping to power product features and tools for geographically diverse global companies.
Our Command R-series family of models leads the scalable market category, focused on balancing high efficiency with unparalleled accuracy, enabling businesses to move beyond proof-of-concept, and into production with AI.
1/ Nabila Abraham introduces a detailed guide on implementing semantic search using OpenSearch and Cohere, a powerful combination for searching large data sets. Follow the link for a comprehensive demo: 🔍 txt.cohere.com/semantic-searc…
2/ The demo demonstrates how to leverage OpenSearch's support for vector search and Cohere’s high-quality embeddings to improve text search capabilities. This brings more context and relevance to search results than traditional keyword-based methods. 💡
3/ The tutorial includes step-by-step instructions to set up an OpenSearch instance, embed documents using Cohere, create an index for your documents, and query similar documents using Cohere embeddings. 🔥
1/5: Interested in Transformer Models in machine learning? They are incredibly good at keeping track of context, and this is why the text that they write makes sense. Check out this video for more on their architecture and functionality:
2/5: Introduced in 'Attention is All You Need', Transformer Models have multifaceted use from writing creative content to human interaction, owing to their architecture. For a deeper dive into the components of these models: visit LLM University: docs.cohere.com/docs/transform…
3/5: Transformers have this unique ability to keep track of the context of what is being written, ensuring meaningful and coherent text generation. This sets them apart from usual text generation models that lack the context understanding.
1/ 🚀 Exciting news! Cohere's multilingual embedding model now enables cross-lingual text classification in 100+ languages! 🌟 Read our latest blog post by @Nils_Reimers, @amrmkayid, & Elliott Choi to learn how you can leverage this groundbreaking tech: txt.cohere.ai/cross-lingual-…
2/ With this model, you can excel in sentiment analysis, content moderation, and intent recognition, all while outperforming the alternatives! 💪🎯
3/ Forget the hassle of collecting training data in each language individually! 😅 With Cohere's multilingual model, you just need a training dataset in a single language to work automatically across 100+ languages. 🤯
1/ 🤔 Should we care about machine learning model interpretability? Professor @hima_lakkaraju tackles questions about model understanding and its implications for real-world use cases of large language models. 🌐
@hima_lakkaraju 2/ 🎓 Harvard Prof. Lakkaraju demonstrates TalkToModel, an interactive dialogue system that explains ML models through conversations. 🗣️ This system shows a compelling conversational explainable user interface (XAI).
@hima_lakkaraju 3/ 💡 In the session, she discusses the importance of model understanding in high-stakes decision-making and how to achieve it. She also explores the LIME Explainability method and addresses the challenges when explainability methods disagree. ❗
2/ 📚 Transformers were introduced in the paper "Attention is All You Need" & can do amazing things like writing stories, answering ❔s, & even passing exams! 🎓 They're great at keeping track of context, which is why their generated text makes sense.😮 arxiv.org/abs/1706.03762
3/ 📱 Picture your phone suggesting words as you type a message. Now imagine a model that can generate coherent text instead of just random suggestions! That's what transformers do, and they do it word by word! 🚀
(1/12) 🚀 Don't fall behind! Stay ahead of the game with March 2023's top NLP papers 📄 Curated by @forai_ml, this list covers the latest advancements in NLP.
Get up to speed with the latest language AI advancements now! 🔥
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