Workshop
Fine-Tuning LLMs: Custom Models for Your Business
Stop renting intelligence. Own a model built for your business.
About this workshop
Go beyond prompting. Fine-tune open-source LLMs on your own data using LoRA and QLoRA. Covers dataset preparation, training runs on Hugging Face, evaluation, and deployment strategies for production-grade custom models.
What you will learn
- Prepare and format instruction-tuning datasets from raw business data
- Run efficient LoRA and QLoRA fine-tuning jobs on Hugging Face with minimal GPU cost
- Evaluate your fine-tuned model against benchmarks and your own quality criteria
- Deploy a custom model endpoint and integrate it into a real application
Who this is for
- ML engineers who want to move beyond OpenAI APIs and own their own models
- Teams with proprietary data who need domain-specific model performance
- Technical leaders evaluating open-source LLMs for cost and compliance reasons
By the end
Before
Paying for GPT-4 API calls on tasks it's not optimised for
After
A custom fine-tuned model that outperforms at a fraction of the cost
Before
Losing data control to closed third-party APIs
After
Your own model running on your own infrastructure
Before
Struggling to get consistent domain-specific outputs
After
A model trained on your data, for your exact use cases
About James
James Okafor
ML Engineer, Hugging Face
Vetted by Maram
James is an ML engineer at Hugging Face where he maintains fine-tuning tooling and writes documentation used by hundreds of thousands of developers. He has fine-tuned over 40 production models across healthcare, legal, and e-commerce domains.
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