中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
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
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要了解的详细信息
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July 2025
作业
7 项作业
授课语言：英语（English）
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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。
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- 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
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
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
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人们为什么选择 Coursera 来帮助自己实现职业发展
Felipe M.
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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.
更多问题
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