Fine-Tuning LLMs: A Practitioner's Guide to Navigating the Pitfalls

Don't let the complexities of LLM fine-tuning derail your projects—this guide provides clear explanations and practical solutions for navigating common challenges.

10
Hours
LUNARTECH
August 2025
Available online
Fine-Tuning LLMs: A Practitioner's Guide to Navigating the Pitfalls

In this course,
you'll learn

Confidently Fine-Tune Any LLM

Acquire the practical skills to fine-tune LLMs for a wide range of applications, achieving superior performance in any domain.

Overcome Technical Roadblocks

Master proven techniques for addressing overfitting, catastrophic forgetting, hallucination, and other common fine-tuning challenges.

Optimize for Efficiency and Impact

mplement strategies to minimize resource consumption and maximize the impact of your fine-tuned models.

Lead with Responsible AI

Build and deploy specialized AI solutions ethically and responsibly, understanding and mitigating potential biases.

Vahe Aslanyan
Your instructor

Vahe Aslanyan

Vahe Aslanyan has empowered hundreds of developers by clarifying complex AI and data science concepts, helping them innovate cutting-edge software solutions.

Trusted by over 10.000 students

Course program

Here's a glimpse of what you'll learn throughout the course

Module 1

Module 1: LLM Fine-Tuning Fundamentals

Gain a foundational understanding of LLM fine-tuning in this module, from its potential to revolutionize AI to the key considerations for successful implementation. Explore real-world applications and learn how to leverage fine-tuning for maximum impact.

Module 2

Module 2: Data: The Core of Effective Fine-Tuning

Effective fine-tuning depends on high-quality data. This module covers dataset selection, bias identification, mitigation techniques, and curation strategies for optimal model performance.

Module 3

Module 3: Addressing Technical Challenges in LLM Fine-Tuning

Learn to navigate technical challenges inherent in fine-tuning LLMs. This module provides practical solutions for overfitting, catastrophic forgetting, knowledge degradation, and hallucination.

Module 4

Module 4: LLM Fine-Tuning Resource Optimization

Fine-tuning demands significant resources. This module explores strategies to optimize resource allocation, reduce costs, and efficiently leverage hardware.

Module 5

Module 5: Ethical Considerations in LLM Fine-Tuning

This module examines the ethical implications of building specialized AI. Learn about bias mitigation, fairness, transparency, and responsible deployment practices.

Module 6

Module 6: Advancements in LLM Fine-Tuning

Explore the evolving landscape of LLM fine-tuning. This module covers current research, emerging trends, and techniques that aim to enhance model robustness, generalization, and the future of AI specialization.

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