TabPFN, or Tabular Prior-data Fitted Network, is a foundation model designed to optimize predictions from tabular datasets by leveraging prior knowledge from various datasets. Initially, TabPFN only supported up to 1,000 training samples and 100 features, which limited its practical application. However, with the release of TabPFN-2.5, the model can now manage nearly 100,000 data points and 2,000 features, making it more suitable for real-world tasks. The model employs in-context learning, allowing it to generalize from a wide range of prior datasets rather than training from scratch for each new dataset. The training process for TabPFN involves generating synthetic datasets, as real-world tabular datasets are often scarce. By using a structural causal model, TabPFN creates diverse datasets that help the model learn general patterns without overfitting. During training, it evaluates predictions against held-out test values and minimizes loss through backpropagation across millions of synthetic datasets. At inference, TabPFN applies the trained model to real datasets without any retraining, enabling zero-shot predictions. TabPFN’s architecture adapts the transformer model for tabular data, treating each table entry as an individual unit. It employs a two-stage attention mechanism to understand relationships within a single row and across multiple rows. This design allows the model to handle varied table sizes and structures effectively. The article also guides through the implementation of TabPFN-2.5, comparing its performance with a standard XGBoost classifier using a Kaggle competition dataset focused on predicting rainfall probabilities. The straightforward integration with a scikit-learn style interface makes it accessible for those familiar with Python.