What is the difference between fine-tuning and training LLM?

Training builds a language model from scratch using massive datasets, while fine-tuning adapts a pre-trained model to specific tasks or domains, requiring less data and compute power.

Which LLM is most in demand?

Models like OpenAI’s GPT series, Google’s PaLM, Meta’s LLaMA, and Falcon-7B are in high demand due to their capabilities and wide adoption in enterprise GenAI solutions.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

LLM Fine-Tuning and Customization Training

LLM Fine-Tuning and Customization Training

本课程是 LLM Application Engineering and Development Certification 专项课程的一部分

位教师：Priyanka Mehta

包含在中

了解更多

2个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

5 小时完成

灵活的计划

自行安排学习进度

2个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

5 小时完成

灵活的计划

自行安排学习进度

您将学到什么

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

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

7 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 LLM Application Engineering and Development Certification 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有2个模块

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.

You should have a solid background in Python, deep learning fundamentals, and prior exposure to large language models. By the end of this course, you will be able to: - Fine-tune LLMs using PEFT, RLHF, and supervised methods - Prepare datasets and optimize hyperparameters for tuning - Evaluate and deploy fine-tuned models using GenAI frameworks - Apply tuning concepts in real-world use cases like Falcon-7B and DALL·E apps Ideal for AI developers, ML engineers, and GenAI researchers.

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.

涵盖的内容

13个视频1篇阅读材料3个作业

13个视频总计62分钟

Learning objectives 2分钟
Basics of LLM Fine-Tuning 3分钟
Fundamentals of Fine-Tuning of LLM 4分钟
Data Preparation for LLM Tuning 6分钟
Demo: Data Preparation for Fine-Tuning 14分钟
Various Methods of Fine-Tuning 5分钟
Supervised Fine-Tuning 3分钟
Parameter-Efficient Fine-Tuning (PEFT) 5分钟
Parameter-Efficient Fine-Tuning (PEFT) Methods 6分钟
Reinforcement Learning with Human Feedback (RLHF) 3分钟
RLHF: Examples and Applications 5分钟
Hyperparameter Tuning in Fine-Tuning 2分钟
Hyperparameter Tuning 5分钟

1篇阅读材料总计10分钟

Course Syllabus 10分钟

3个作业总计70分钟

Assessment for Foundations of LLM Fine-Tuning 40分钟
Quiz on Introduction to LLM Fine-Tuning 15分钟
Quiz on Fine-Tuning Methodologies 15分钟

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.

涵盖的内容

10个视频4个作业

10个视频总计62分钟

Optimizing and Evaluating Fine-Tuned Models 4分钟
Fine-Tuning and Hyperparameter Optimization 1分钟
Fine-Tuning Large Language Models 3分钟
LLM Fine-Tuning Frameworks 1分钟
LLM Fine-Tuning: Key Libraries and Platforms 4分钟
Data Preparation, Model Selection and Monitor Overfitting 6分钟
Hyperparameter Tuning, Bias Awareness and Model Evaluation 4分钟
Demo: Finetune Falcon-7b Own Instance LLM 27分钟
Demo: Build an Image Generation App with LangChain and OpenAI DALL-E 9分钟
Key Takeaways 1分钟

4个作业总计85分钟

Assessment for Evaluation, Implementation, and Best Practices 40分钟
Quiz on Evaluation of Fine-Tuning Model 15分钟
Quiz on Hands-on Fine-Tuning 15分钟
Quiz on Fine-Tuning Best Practices 15分钟

获得职业证书

将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。

位教师

Priyanka Mehta

Simplilearn

81 门课程 60,339 名学生

提供方

Simplilearn

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常见问题

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.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.