Model Type: Transfer Learning
What it does: Customizes a pre-trained AI model to do a new task — using your own small dataset.
Media: Image, Text, Sound (depending on the picked model

Fine-tuning is like taking a model that already knows a lot, and teaching it something new. Imagine a model trained on millions of images — it already understands shapes, objects, styles. With fine-tuning, you can show it your own images and make it specialize in your unique style, subject, or task.

How It Works (Simple Version)

You start with a pretrained model — for example:

• An image model that knows how to recognize 1,000 everyday objects
• A text model that knows how to generate general English sentences
• A diffusion model that knows how to generate images from text

You feed it your own small dataset (10–100 examples), and it updates just enough to specialize in your data — while still relying on everything it already knows.

To dos

1. Pick a model to fine-tune

Choose what kind of thing you want to teach it:

• An image generator (like Stable Diffusion)
• A text model (like GPT-2)
• A classifier (like MobileNet)

2. Collect your dataset

• Typically 10–100 images or text examples are enough
• Should be as consistent as possible (size, format, labeling)
• Example: 20 portraits of yourself, or 50 scanned doodles

3. Fine-tune the model

• You’ll use a Colab notebook or tools like Hugging Face AutoTrain
• You don’t need to code, but you’ll follow setup steps
• Training usually takes 20–60 minutes

4. Test and reflect

• What changed in the model’s behavior?
• What did it learn? What did it forget?
• Try different prompts or inputs to explore the result

Resources