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DeepLearning.AI

Nikita Namjoshi

In this course, you will gain a conceptual understanding of the RLHF training process, and then practice applying RLHF to tune an LLM. You will explore the two datasets that are used in RLHF training: the 'preference' and 'prompt' datasets. Use the open source Google Cloud Pipeline Components Library to fine-tune the Llama 2 model with RLHF. Assess the tuned LLM against the original base model by comparing loss curves and using the 'Side-by-Side (SxS)' method.

Reinforcement Learning from Human Feedback

Get a conceptual understanding of Reinforcement Learning from Human Feedback (RLHF) and its importance in aligning LLMs with human values and preferences.

Introduction to RLHF

Explore the two datasets used in RLHF training: the 'preference' and 'prompt' datasets.

Datasets for RLHF

Learn how to use the open source Google Cloud Pipeline Components Library to fine-tune the Llama 2 model with RLHF.

Using Google Cloud Pipeline Components Library

Assess the tuned LLM against the original base model by comparing loss curves and using the 'Side-by-Side (SxS)' method.

Model Evaluation

Get a conceptual understanding of Reinforcement Learning from Human Feedback (RLHF), as well as the datasets needed for this technique. Fine-tune the Llama 2 model using RLHF with the open source Google Cloud Pipeline Components Library and evaluate tuned model performance against the base model with evaluation methods.

Anyone with intermediate Python knowledge who’s interested in learning about using the Reinforcement Learning from Human Feedback technique.

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Reinforcement Learning from Human Feedback

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