Generative AI Engineering and Fine-Tuning Transformers

IBM connects pretrained transformer use with downstream fine-tuning and parameter-efficient methods. Hugging Face and PyTorch examples cover loading, inference and training. It suits learners who understand Python and transformer architecture and want to compare full training requirements with adapter-based approaches on a clearly defined task.

What you will learn

• Use pretrained models for downstream tasks.
• Explore LoRA and QLoRA approaches.
• Work with Hugging Face and PyTorch.

Who this suits

Basic Python, PyTorch, transformer architecture and neural-network knowledge are 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

Compare two tuning configurations using the same data split and record memory use, evaluation behavior and the tradeoffs that matter for the intended task.

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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