Generative AI Advanced Fine-Tuning for LLMs

IBM develops advanced tuning through instruction data, reward models and preference-based methods. Hugging Face examples connect the concepts with training workflows. It suits learners with LLM and machine-learning foundations who want to understand how objectives differ, while recognizing that a better training score does not establish broader model quality.

What you will learn

• Explore instruction tuning and reward models.
• Examine RLHF and PPO workflows.
• Apply direct preference optimization concepts.

Who this suits

Basic knowledge of LLMs, instruction tuning, reinforcement learning and neural-network concepts is recommended.

Before you enroll

Training may require paid APIs, suitable GPU hardware or cloud credits. Check current model availability, licenses and library compatibility before enrolling. Use permissioned, non-sensitive datasets and keep evaluation examples separate from training data. Compare the tuned model with the baseline; tuning does not guarantee better accuracy or reliable behavior.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Provider access terms can change; review them before paying.

Put it into practice

Define an evaluation set for a fictional task before tuning and compare model outputs against explicit criteria, including examples where the tuned model regresses.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

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