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AI Engineer World's Fair (2024)

Deep Dive: Building SOTA Open Weights Tool Use: The Command R Family


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Sandra Kublik @itsSandraKublik / Cohere
Watch it on YouTube | AI.Engineer Talk Details

Sandra from Cohere gave a talk about their recent work on language models, focusing on their Command R family of models. She highlighted their progress in developing models optimized for retrieval-augmented generation (RAG) and tool use.

Key Points


  • Cohere released Command R and Command R Plus models in March 2024
  • These models excel at structured reasoning and are competitive with larger models like GPT-4 Turbo
  • Command R Plus quickly gained popularity in the open-source community
  • Cohere focused on optimizing for citations and reducing hallucinations
  • They open-sourced their UI toolkit for RAG applications in April 2024
  • Recent work has centered on improving tool use capabilities, especially for enterprise contexts


Technical Details

Model Design Decisions


  • Optimized for retrieval-augmented generation (RAG) and tool use
  • Addressed challenges like prompt sensitivity and overcoming model bias towards focusing on the beginning of documents
  • Improved ability to balance pre-trained knowledge with new information from prompts
  • Enhanced citation capabilities for better transparency and reduced hallucinations


Tool Use Capabilities


  • Developed both single-step and multi-step tool use functionality
  • Multi-step capability allows for sequential reasoning and error correction
  • Released a multi-step API that allows users to describe available tools and parameters
  • Model creates plans and adapts them based on tool outputs


Performance and Efficiency



  • Command R Plus is competitive with GPT-4 Turbo on complex reasoning benchmarks
    3-5 times cheaper to run than comparable models, improving scalability for production use


Sandra demonstrated two applications built with Cohere's models:


Complexity AI (cplxai): A generative search engine that provides answers grounded in multiple sources with clickable citations.


Internal RAG demo: Showed multi-step reasoning capabilities by asking the model to find the three largest companies by market cap, get employee counts, create a graph, and draft a tweet. The demo displayed each step of the model's thought process.


As well as usage with their Discord bot.