This LLM Fine-Tuning course equips you with the skills to optimize and deploy domain-specific large language models for advanced Generative AI applications. Begin with foundational concepts—learn supervised fine-tuning, parameter-efficient methods (PEFT), and reinforcement learning with human feedback (RLHF). Master data preparation, hyperparameter tuning, and key evaluation strategies. Progress to implementation using LLM frameworks and libraries, and apply best practices for model selection, bias monitoring, and overfitting control. Conclude with hands-on demos—fine-tune Falcon-7B and build an image generation app using LangChain and OpenAI DALL·E.
LLM Fine-Tuning and Customization Training
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LLM Fine-Tuning and Customization Training
This course is part of LLM Application Engineering and Development Certification Specialization
Instructor: Priyanka Mehta
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Beginner level
Recommended experience
4 hours to complete
3 weeks at 1 hour a week
Flexible schedule
Learn at your own pace
What you'll learn
Fine-tune LLMs using supervised learning, PEFT, and RLHF techniques
Prepare and structure datasets for efficient model training
Optimize model accuracy with hyperparameter tuning and bias checks
Build real-world GenAI apps with fine-tuned models like Falcon-7B and DALL·E
Skills you'll gain
Details to know
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Recently updated!
July 2025
Assessments
7 assignments
Taught in English
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This course is part of the LLM Application Engineering and Development Certification Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 2 modules in this course
Explore the foundations of LLM fine-tuning in this comprehensive module. Learn core principles of large language model (LLM) fine-tuning, from supervised and parameter-efficient methods (PEFT) to reinforcement learning with human feedback (RLHF). Gain hands-on experience in data preparation and hyperparameter tuning through real-world demos to optimize GenAI performance.
What's included
13 videos1 reading3 assignments
Master LLM fine-tuning evaluation and deployment in this hands-on module. Learn to optimize and assess fine-tuned models, explore key libraries and frameworks, and implement best practices for data preparation, model selection, and bias monitoring. Apply concepts in real-time through demos including tuning Falcon-7B and building an AI image generation app with LangChain and DALL·E.
What's included
10 videos4 assignments
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Frequently asked questions
Start by understanding the basics of large language models and their architecture. Then explore fine-tuning techniques like supervised learning, PEFT, and RLHF using tools such as Hugging Face, LangChain, and frameworks like PyTorch.
The time required depends on model size, dataset, and infrastructure. Fine-tuning smaller models can take a few hours, while larger models like Falcon-7B may require several days on high-performance GPUs.
A hands-on course that covers LLM architecture, fine-tuning methods, and real-world deployment—such as Generative AI programs with practical demos on Hugging Face and LangChain, is ideal for mastering LLMs.
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