Reinforcement Learning from Human Feedback
Free for a limited time
- 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
- Evaluate tuned model performance against the base model with evaluation methods
What you’ll learn in this course
Large language models (LLMs) are trained on human-generated text, but additional methods are needed to align an LLM with human values and preferences.
Reinforcement Learning from Human Feedback (RLHF) is currently the main method for aligning LLMs with human values and preferences. RLHF is also used for further tuning a base LLM to align with values and preferences that are specific to your use case.
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.
Who should join?
Anyone with intermediate Python knowledge who’s interested in learning about using the Reinforcement Learning from Human Feedback technique.
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