Unlock Enterprise-Grade Performance: Fine-tuning Cohere Command R

Greetings Cohere Community,

Becoming an expert in any field takes years of hard work and practice. Whether it's learning a new language, understanding quantum physics, or mastering negotiation, the path to expertise can be long. But what if we could accelerate this process? That’s what fine-tuning can do for large language models (LLMs) — a technique that enables AI systems to achieve super-smart levels of proficiency in specific tasks.?

In this newsletter, we delve into the intricacies of fine-tuning Cohere Command R, now available on the Cohere platform and Amazon SageMaker. We explore its cost and performance benefits, look at how it improves retrieval-augmented generation (RAG) solutions, and highlight industries where fine-tuning delivers outsized value.?

How Fine-Tuning Works

Fine-tuning involves taking a foundational model, which already has a broad understanding of language, and further training it on smaller, task-specific, labeled datasets. The process sharpens the model's performance in particular areas, ensuring that it can handle specialized queries and tasks with greater accuracy and efficiency.

Trained for enterprise-grade workloads, Command R can be customized even further by tweaking up to five hyperparameters, thus modifying the key settings that control how the model is trained. For example, customers can change the learning rate, batch size, or number of training cycles to better fit their needs. For a complete guide on how to fine-tune, read our Developer’s Guide to Fine-Tuning.?

When to Fine-Tune

The decision to fine-tune a model hinges on a cost-performance analysis. Optimizing Command R performance can be up to 15x more affordable than other industry-leading models.

Many enterprise use cases quickly move past the general versatility of applications to requiring high accuracy for specific tasks. For example, a legal document analysis tool benefits immensely from using a model that’s been fine-tuned on legal texts. On average, a fine-tuned model demonstrates a 20% performance improvement when compared to the baseline model.?

According to Erik Bergenholtz, VP of AI Strategy and Operations at Oracle, “Cohere’s fine-tuned models even outperform out-of-the-box versions of much larger and more expensive models.”

Why pay more when you can get optimized performance at a fraction of the cost, with faster response times and improved accuracy in targeted domains? Depending on the desired performance level, it can take as little as a few hours to just a couple of weeks to train a model. And Cohere's team is available to support customers as needed. Feel free to reach out to us to get you started.??

Fine-Tuning for RAG Applications

RAG systems help enterprises build applications that integrate specific, up-to-date knowledge from large datasets, reduce hallucinations, and provide transparency with citations.

Fine-tuning an LLM before using it in RAG applications offers several benefits. While RAG alone can provide relevant information to the LLM, and even perform tasks for end users, fine-tuning the model before using it in a RAG setup can significantly enhance its understanding, reasoning abilities, and the quality of its generated responses within the specific application domain.?

Industries Where Fine-Tuning Excels

Fine-tuning finds its greatest value in industries where specialized knowledge and precision are paramount.?

Legal: Fine-tuned models can parse legal documents, contracts, and case law with high accuracy, aiding lawyers and paralegals.

Finance: In financial analysis, fine-tuned models can dissect, summarize, and generate market reports, predict trends, and provide insights based on historical data.

Retail: Fine-tuning an LLM on a dataset of existing product catalogs and service logs can help retailers generate more accurate, relevant descriptions for customer queries through AI agents or virtual assistants.?

By leveraging the power of fine-tuning, businesses can unlock the full potential of large language models, transforming them into specialized solutions tailored to the unique needs of the business.?

For more, check out our latest articles or read on for this month’s highlights and upcoming events!

Product?

Command R fine-tuning is available on the Cohere platform and Amazon SageMaker. Check out our latest cookbook to get started.?

For Business

Speed up Enterprise AI adoption and discover how to train our brains to use generative AI at work in our latest interview with Conor Grennan, Chief AI Architect at NYU Stern.?

Developers

Start building with Cohere models now available on MongoDB’s new AI Applications Program (MAAP) and deploy generative AI applications at enterprise scale. Plus, we now officially support Java! Check out our GitHub repository.

Research

Cohere For AI launches research model Aya-23: an LLM with 8B and 35B parameter weights release and capabilities in 23 languages. Aya-23 is designed to make state-of-the-art, multilingual, generative AI breakthroughs accessible to the research community.?

Company

Cohere is honored to be recognized in this year's CNBC Disruptor 50 list. At Cohere, we’re focused on ensuring that AI technology delivers on the promise of creating a transformative impact for businesses.

Upcoming events with Cohere?

• June 4 [Online] Join Cohere For AI in conversation with Dr. Matthias Treder, ML Engineer at Sony discussing adaptive render-video streaming for virtual environments.
• June 6 [Online] Join Cohere For AI for some speed brainstorming and open networking in their second Roads to Research online event.
• June 7 [Online]: Join Cohere For AI and Meta’s Lucas Lehnert as they explore the topic of better planning with transformers.?
• June 17 - 20 [Toronto] We are back at Collision! Cohere Co-Founder and CEO Aidan Gomez will speak on the main stage.?

For all upcoming events, explore cohere.com/events.?