Fine-Tune Vision AI Models That Beat GPT-4 | Fine Tuning Gemma 3 4B with Datawizz

Learn how to train specialized vision models that outperform GPT-4.1 while being faster and cheaper! In this comprehensive tutorial, I'll show you how to fine-tune the Gemma 3 4B model on the Datawizz platform to create a food recognition AI that extracts dish names, ingredients, nutritional info, and portion sizes from images. We'll use the MMFood100K dataset and create custom evaluators to benchmark our model against GPT-4.1, proving that smaller, specialized models can deliver better results for domain-specific tasks. 🚀 What You'll Learn: - Fine-tuning vision models on custom datasets - Creating structured prompts for JSON outputs - Building custom evaluation metrics - Benchmarking against GPT-4.1 and other models - Deploying production-ready AI endpoints 📊 Results Preview: Our fine-tuned 4B parameter model beats GPT-4 in accuracy, runs 50% faster, and costs significantly less! ⏱️ Timestamps: 00:00 Introduction & Demo Overview 00:45 Dataset Overview (MMFood100K from Hugging Face) 01:33 Creating the Prompt Template in Datawizz 04:10 Importing & Preparing the Dataset 07:10 Fine Tuning the Model 09:09 Training Results & Loss Curves 10:20 Manually Testing the Model 12:44 Creating Custom Evaluators 19:36 Running Full Evaluation Suite 21:10 Benchmark Results & Analysis 24:00 Creating Production Endpoints 25:04 Summary & Conclusion 🔗 Resources: Datawizz: https://datawizz.ai MMFood100K Dataset: https://huggingface.co/datasets/Codatta/MM-Food-100K Written Tutorial & Code: https://datawizz.ai/blog/fine-tuning-gemma-3-with-multi-modal-vision-images-inputs 💡 Key Takeaways: Specialized models outperform generic LLMs for domain tasks Fine-tuning can achieve better accuracy with 100x fewer parameters Custom evaluators enable precise performance measurement DataWiz simplifies the entire ML workflow from data to deployment 🏷️ Tags: #AIFinetuning #VisionAI #Datawizz #MachineLearning #GPT4 #Gemma #ComputerVision #FoodRecognition #AITutorial #MLOps #ModelTraining #AIDepl