Fine Tune Gemma 3 with Hugging Face and Datawizz | Tutorial
Learn how to fine-tune and evaluate a Gemma 3 270M model using the Datawizz platform in this step-by-step tutorial. We'll train a model to translate English sentences into Yoda-speak, then benchmark it against GPT-4.1 and GPT-4.1 Mini.
In this tutorial, you'll learn:
- How to prepare and format datasets for model training
- Fine-tuning small language models on custom datasets
- Creating train/test splits for proper evaluation
- Deploying and comparing multiple models
- Running automated benchmarks and performance tests
00:00 - Introduction to fine-tuning with Datawizz
00:07 - Downloading an…
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Chapters (9)
Introduction to fine-tuning with Datawizz
0:07
Downloading and preparing the dataset
1:51
Creating train/test data splits
2:07
Model training setup (Gemma 270M base model)
2:35
Training configuration and launch
3:00
Training results and loss curves
4:03
Manual model testing and comparison
5:13
Configuring evaluation metrics (string equality, word error rate)
6:53
Export and deployment options
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