Fine-Tuning and Customizing Open-Source LLMs: LoRA, QLoRA and Self-Hosting
Read a free preview of the first lesson No account, no card · the opening of Lesson 1, plus the interactive platform demo and the AI Professor Start now Finish the course with a publicly verifiable Certificate of Completion See the certificateA premium, complete and advanced course on fine-tuning and customizing open-source large language models, updated for 2026. You will learn when to fine-tune versus RAG versus prompting, the anatomy of modern open models (Llama, Mistral, Qwen, DeepSeek, Gemma 4) and their licenses, how to build high-quality and legally sound datasets, parameter-efficient fine-tuning with LoRA and QLoRA (rank, alpha, target modules, quantization with bitsandbytes), the full toolchain (Hugging Face transformers, PEFT, TRL, Unsloth, Axolotl), instruction tuning, chat templates and preference optimization (DPO), evaluation, inference quantization (GGUF, GPTQ, AWQ), and self-hosting with Ollama, vLLM and TGI. Includes cost and hardware planning, production deployment, real-world case studies, a strong focus on legal and ethical fine-tuning data, and a comprehensive final assessment.
What you will learn
Practical skills you gain by completing this course
Who it is for
Recommended level
Assumes hands-on experience with AI and complex scenarios.
Updates
Regular
Last update: Aug 8, 2026. Content kept up to date.
Category
IT & Engineering
A technical course for IT professionals — available with individual course access or the IT Pro / All Access bundle.
Advanced level
Hands-on experience required
Assumes practical experience with AI. Covers complex scenarios and advanced strategies.
Always up to date
Last update: Aug 8, 2026
The course is updated regularly with the latest information, tools and practices from the industry.
Practical and applied
30 lessons with real examples
Each lesson includes practical scenarios, actionable checklists and quizzes to check your understanding.
Certificate of Completion
You leave with proof anyone can check
for the course Fine-Tuning and Customizing Open-Source LLMs: LoRA, QLoRA and Self-Hosting
A private attestation with a unique number and a QR code. A recruiter confirms it is authentic in a second, on the public verification page — no account, no cost.
What your certificate states for this course
- When to Fine-Tune: Foundations & Decision Framework
- Data: The Foundation of Fine-Tuning
- Parameter-Efficient Fine-Tuning (PEFT): LoRA
- QLoRA and Quantized Training
- The Fine-Tuning Toolchain
- Instruction Tuning & Alignment
A private attestation of course completion. It is NOT a diploma or a state-recognised qualification under Romanian Laws 198/2023, 199/2023 or Government Ordinance 129/2000; employers may take it into account at their own discretion. Certification policy.
Anyone can verify it
Public verification page, QR code and cryptographic fingerprint — no account and no cost for the person checking.
Useful when job hunting
Add it to your CV and LinkedIn profile; a recruiter confirms authenticity with one click.
Transparent
It shows exactly what it attests: the course, the modules covered, the assessment score and the completion date.
Curriculum
10 modules, 30 lessons — structured to learn step by step.
When to Fine-Tune: Foundations & Decision Framework
3 lessonsData: The Foundation of Fine-Tuning
3 lessonsParameter-Efficient Fine-Tuning (PEFT): LoRA
3 lessonsQLoRA and Quantized Training
3 lessonsThe Fine-Tuning Toolchain
3 lessonsInstruction Tuning & Alignment
4 lessonsEvaluation and Quantization for Inference
3 lessonsSelf-Hosting and Serving
4 lessonsProduction, Cost, and Case Studies
3 lessonsFinal Quiz — Fine-Tuning Open-Source LLMs
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