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Foundation models (FMs), like LLMs, have shown impressive capabilities across many natural language tasks. However, directly utilizing these models for applications can present challenges. In this session, explore and compare methods for effectively adapting foundation models for your applications. Learn how to evaluate prompt engineering, which reformulates the task into natural language, and fine-tuning, which updates the models' parameters on new tasks and use cases. Explore the trade-offs between usability and resource requirements for each method. Learn how to productively use the capabilities of large models, and discover future improvements to their adaptability.
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