Скачать или смотреть 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...
Written Tutorial & Code: https://datawizz.ai/blog/fine-tuning-gemma...

💡 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 #AIDeployment

💬 Drop a comment if you have questions or want to see more specialized model tutorials!
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