Unlock the secrets of maximizing language models' potential in our exclusive sessions that traverse the diverse landscapes of language model fine-tuning. Dive deep into the intricacies of the fine-tuning process, explore the best practices crucial for training large models, and witness real-world applications through the lens of the prestigious Kaggle competition.
Our sessions promise an insightful exploration of new evaluation paradigms, shedding light on how to truly comprehend what these models learn. Discover the answers to fundamental questions and challenges in fine-tuning, as seasoned experts share practical experiences, invaluable tips, and unique insights.
Don't miss this opportunity brought to us by Weights & Biases to expand your knowledge and harness the full potential of Language Models! Join us to gain an enriched understanding and learn to leverage these models effectively. Register now and be at the forefront of cutting-edge advancements in Language Model Fine-Tuning!
This competition challenged participants to submit a model capable of answering science-related multiple-choice questions. In doing so it provided a fruitful environment for exploring most of the key techniques and approaches being applied today by anyone building with LLMs. In this talk, we'll look at some key lessons that this competition can teach us.
AI models have become orders of magnitude larger in the last few years.
Training such large models presents new challenges, and has been mainly practiced in large companies.
In this talk, we tackle best practices for training large models, from early prototype to production.
In his session Thomas will focus on understanding the ins and outs of fine-tuning LLMs. We all have a lot of questions during the fine-tuning process. How do you prepare your data? How much data do you need? Do you need to use a high-level API, or can you do this in PyTorch? During this talk, we will try to answer these questions. Thomas will share some tips and tricks on his journey in the LLM fine-tuning landscape. What worked and what did not, and hopefully, you will learn from his experience and the mistakes he made.
Leap Labs demonstrates how data-independent model evaluations represent a paradigm shift in the model development process. All through our dashboard’s beautiful Weights & Biases Weave integration.
