LLM Fine-Tuning for Beginners: HuggingFace & Unsloth

Karthik KK develops transformer use and evaluation before moving into full and parameter-efficient tuning. BERT, question-answering data and Unsloth appear in the wider sequence. It suits learners beginning practical model customization who already know basic Python, with particular value in checking unseen examples and comparing the behavior of trained and baseline models.

What you will learn

• Use Hugging Face models and datasets.
• Prepare and evaluate tuning workflows.
• Explore full tuning, LoRA and QLoRA.

Who this suits

Basic Python and programming concepts are required; familiarity with an AI tool is also listed.

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.

Udemy offers paid purchase or subscription access depending on the current course and region. Paid teaching courses provide completion certificates under Udemy’s policy. Check current access terms and any separate software or material costs before enrolling.

Put it into practice

Inspect a small training dataset for duplicated examples and create a separate evaluation split that cannot be answered by memorizing training records.

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.

Where a formal level is unavailable, the level filter is an editorial estimate based on the published audience and requirements. Duration filters reflect the listed video time; exercises and independent practice can take longer.

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Supporting provider sources

• support.udemy.com: supporting course information