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Fine-Tuning LLMs

Fine-tuning large language models allows you to adapt pre-trained models to specific tasks and datasets. This course covers parameter-efficient fine-tuning techniques like LoRA, QLoRA, and adapters using libraries like Hugging Face Transformers and PEFT. You will be able to implement and evaluate fine-tuned models for improved performance on your target applications.

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Lessons
4
Price
Free
Course Curriculum

What you'll learn

2 Modules
4 Lessons
~20m total
01
01
Module 01
Parameter-Efficient Fine-Tuning
This module covers parameter-efficient fine-tuning techniques, focusing on LoRA and QLoRA. You'll learn how to implement these methods using Hugging Face Transformers and PEFT to adapt pre-trained LLMs to specific tasks with reduced computational costs.
2 lessons10 min
0%
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02
02
Module 02
Evaluating Fine-Tuned Models
This module focuses on evaluating the performance of fine-tuned models using appropriate metrics and techniques. You'll learn how to assess the effectiveness of different fine-tuning strategies and compare their results.
2 lessons10 min🔒 Locked
0%
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Fine-Tuning LLMs
30/05/2026
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