Tired of LLMs giving you generic responses that miss the mark? In this video, we’ll explain how to train and fine-tune large language models on your data. Whether you’re a developer, data scientist, or tech team lead, this tutorial breaks down the entire process into actionable steps.
🚀 Get the data you need with our Web Scraping API: https://decodo.com/scraping/web?utm_source=youtube&utm_campaign=explainer
What you’ll learn:
✅ The difference between training from scratch vs. fine-tuning (and when to use each)
✅ How to prepare high-quality training data
✅ Step-by-step process for training/fine-tuning LLMs on custom datasets
✅ Essential tools and infrastructure setup
✅ Best practices for evaluation, deployment, and monitoring
Timestamps:
00:00 Training vs Fine-Tuning: What’s the Difference?
01:04 Requirements for LLM Training
01:27 What Determines Training Data Quality
02:08 8 Steps of LLM Training/Fine-Tuning
FAQ:
❓ How much data do I need for fine-tuning?
There’s no magic number: it depends on your task complexity and model size. You can achieve good results with surprisingly little data, if it’s high-quality and consistent.
❓ What’s the difference between training and fine-tuning?
Training builds an LLM’s knowledge from scratch using massive datasets and resources. Fine-tuning starts with a pre-trained model and adapts it using your specific data.
❓ How do I know if my model is overfitting?
Monitor your validation metrics during training. If training accuracy looks great but validation performance drops, that’s overfitting. Use a separate validation dataset to catch this early.
❓ What tools do I need to get started?
Essential tools include Python, PyTorch, TensorFlow, and Hugging Face Transformers. Keep everything modular and version-controlled.
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🔹 GitHub: github.com/decodo
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