Optimising open source LLMs: fine-tuning, reinforcement learning and deployment | Building MQube
Автор: MQube
Загружено: 2025-12-03
Просмотров: 76
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!
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