LLM Fine-Tuning: Train an AI Model on Your Data

_*Disclaimer:* This video features an AI-generated avatar of one of our own Oxylabs experts, created with their full knowledge and consent. The voice and likeness are their own, recreated with AI. The video itself was produced using AI to make our production process more efficient while keeping the people and expertise behind them genuine._ ⚒️*OXYLABS WEB SCRAPER API* 👉 https://oxy.yt/dWM3 LLM fine-tuning sounds like something you'd need a research lab for – but it's really just a few hundred lines of code and the right dataset. In this hands-on tutorial, we scrape a custom dataset off the web and use it to fine-tune an open-source model. We build a real example end to end: an Amazon product title generator. You'll see how to scrape 10,000 products in about five minutes with the Oxylabs Web Scraper API, clean the dataset so repetition doesn't poison training, and run the actual fine-tune on a free GPU. This is a practical, code-along guide to LLM fine-tuning – no research background required. We cover the decision that comes before any training run: fine-tuning vs RAG, and when each one is the right call. Then we walk through the method choice – LoRA fine tuning versus QLoRA, and why LoRA is the better fit when your model already fits in memory. We get concrete about hardware requirements too, using Kaggle's free dual-T4 GPUs so you can follow along without your own rig. The part most tutorials skip is where the data comes from. A model is only as good as the dataset behind it, and building a proper LLM fine-tuning dataset means scraping publicly available web data at scale, then cleaning it. That's the gap we close here – from a page on the web to a workflow you can adapt to Qwen, or any open model, for your own use case. Everything we cover applies whether you're doing AI fine-tuning for a hobby project or a production system. Full code and data samples are on GitHub, so you can grab everything and build your own. 📚 *ADDITIONAL RESOURCES* ✏️ Code + dataset sample (GitHub): https://oxy.yt/3EhE ✏️ Oxylabs Web Scraper API docs: https://oxy.yt/tW1F 🎥 *WATCH NEXT* 🔴 AI Web Scraping Tutorial: https://oxy.yt/NW0Y 🔴 Data Grounding in AI: https://oxy.yt/0W23 🔧 *OUR SCRAPING SOLUTIONS* Residential Proxies: 👉 https://oxy.yt/sW9S Mobile Proxies: 👉 https://oxy.yt/VW3p ISP Proxies: 👉 https://oxy.yt/oW8k Dedicated ISP Proxies: 👉 https://oxy.yt/1W4V Datacenter Proxies: 👉 https://oxy.yt/qW71 ⏳ *TIMESTAMPS* 0:00 Intro 0:20 The use case 1:24 How to scrape data 2:40 Finding the category IDs 3:11 Scaling to 10,000 products 4:46 Cleaning the dataset 5:26 The training code 8:01 Training the model 9:12 Reading the training logs 11:01 Testing the model 12:25 Base vs. fine-tuned 13:04 Outro 💬 *CONNECT WITH US* 24/7 Support Team: [email protected] Website: oxylabs.io LinkedIn: linkedin.com/company/oxylabs Social: https://x.com/Oxylabs_io © 2026 Oxylabs. All rights reserved. #LLMFineTuning #AIFineTuning #Oxylabs