LLM - Fine tune with custom data

Adnan Waheed connects custom-data tuning with hosted workflows, quantization and examples using Unsloth and LLaMA Factory. The outline includes data extraction and model testing. Some examples reference older hosted models and platforms, making compatibility review important. It suits technical learners who want to compare several tuning routes and understand their data and hardware assumptions.

What you will learn

• Prepare training and validation data.
• Explore quantization concepts.
• Compare hosted and local tuning workflows.

Who this suits

Technical learners ready to inspect Python and model-training examples; check the published setup requirements before enrolling.

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

Use a fictional data-extraction task to define expected outputs and compare base-model errors with tuned-model errors on examples withheld from training.

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