Speaker

The rise of large language models (LLMs) has opened up exciting possibilities for developers looking to build intelligent applications. However, the process of adapting these models to specific use cases can be difficult, requiring deep expertise and substantial resources. In this talk, we'll introduce you to InstructLab, an open-source project that aims to make LLM tuning accessible to developers and engineers of all skill levels, on consumer-grade hardware.

We'll explore how InstructLab's innovative approach combines collaborative knowledge curation, efficient data generation, and instruction training to enable developers to refine foundation models for specific use cases. Through a live demonstration, you’ll learn how to enhance an LLM with new knowledge and capabilities for targeted applications, without needing data science expertise. Join us to explore how LLM tuning can be more accessible and democratized, empowering developers to build on the power of AI in their projects.

As large language models (LLMs) become more accessible, running them locally unlocks exciting opportunities for developers, engineers, and privacy-focused users. Why rely on costly cloud AI services that share your data when you could deploy your own models tailored to your needs? In this session, we’ll dive into the advantages of local LLM deployment, from selecting the right open source model to optimizing performance on consumer hardware and integrating with your unique data.

Let’s explore the journey to your own local stack for AI, and cover the important technical details such as model quantization, API integrations with IDE code assistants, and advanced methods like Retrieval-Augmented Generation (RAG) to connect your LLM to private data sources. Don’t miss out on the fun live demos that prove the bright future of open source AI is already here!