Optimising open source LLMs: fine-tuning, reinforcement learning and deployment | Building MQube скачать в хорошем качестве

Optimising open source LLMs: fine-tuning, reinforcement learning and deployment | Building MQube

Learn how to customise open-source LLMs using LoRA and reinforcement learning, and explore the advantages of open-source models for data control and privacy. In this video, Rafay, Senior Machine Learning Engineer, explores how to customise open-source large language models (LLMs) for specific use cases. He explains the powerful LoRA (Low-Rank Adaptation) technique, which reduces hardware requirements, and delves into how reinforcement learning can help align LLMs with real user needs. We’ll also discuss the advantages of open-source LLMs over closed-source alternatives, including greater control over data, privacy, and deployment flexibility. Learn more about how we’re using cutting-edge frameworks to deploy and customise these robust models, and how these techniques can drive innovation and improve model performance! We’re sharing more of what we’re building at MQube to create a smarter way to originate mortgages. We’ve launched a blog where you’ll find our latest products, the challenges we’re tackling, and a behind-the-scenes look at life in a startup. Explore more at   / building