Enhancing LLMs with fine-tuning and resource optimization
Large Language Models (LLMs) have surged in popularity over recent years, emerging as a frontier in the field of artificial intelligence. Powered by advancements in machine learning and vast amounts of data, LLMs exhibit impressive linguistic abilities and we are seeing many applications spanning content creation, advanced chatbots, refining search engine results, and assisting researchers by summarizing extensive data.
Language model fine-tuning, particularly with large language models (LLMs), has emerged as a critical process to customize and enhance the capabilities of pre-trained models. The intricacies of LLMs require a robust infrastructure - and declarative orchestration offers a systematic approach to manage and optimize LLM tasks efficiently.
Whether you're new to machine learning or an experienced practitioner, this webinar will give you the conceptual understanding of fine-tuning, educate you on the different methodologies, share techniques for parameter optimization, and show you how to do it efficiently with Union as the underlying orchestrator.
Key discussions will include
- Methods of fine-tuning LLMs for specific domains and tasks along with the necessary infrastructure to do so
- Techniques for parameter efficient fine-tuning with qLoRA and improving LLM performance through resource optimization
- Benefits of using Union as a platform for simplifying the fine-tuning process of LLMs - caching outputs, identifying resource bottlenecks and enhancing visibility of tasks
- Comparison analysis of other tools/solutions for efficient fine-tuning
Join us as we explore best practices and techniques for fine-tuning LLMs with Union.