📺 Watch the full tutorial on YouTube

📊 TabFM + Ollama Analyzer

Zero-Shot Tabular Predictions & Local LLM Insights 🧠⚡

A visually streamlined, end-to-end tool blending TabFM (Tabular Foundation Model) and Ollama (qwen3:4b). Input data, get predictions instantly, and receive AI-driven summaries—no dataset training required. This repository acts as an interface and wrapper for Google's TabFM, ensuring fast tabular predictions running 100% locally.

---

🛠️ Tech Stack

• 🧠 Core ML: TabFM (PyTorch)
• 🤖 Local LLM: Ollama (qwen3:4b - zero temp/fast inference)
• 🖥️ Frontend: Streamlit
• 💾 Data: pandas & numpy

📂 Files

• 📄 README.md: Project overview and setup.
• 🐍 app.py: Streamlit UI, TabFM engine, and Ollama integration.
• 📦 requirements.txt: Minimal Python dependencies.
• 📝 outputs.md / ideal_outputs.md: Auto-generated, identically matched markdown logs of your predictions and AI insights.

---

🚀 Setup & Execution

Copy-paste these commands directly into Windows PowerShell:

1. Install Core Dependencies

git clone https://github.com/google-research/tabfm.git
cd tabfm
pip install -e .[pytorch]
pip install streamlit pandas numpy requests
ollama pull qwen3:4b

2. Run Application

streamlit run app.py

3. Quick Test (Headless)

python -c "from tabfm import tabfm_v1_0_0_pytorch; print('TabFM loaded successfully')"

---

🎯 5 Core Use Cases

1. 🏦 Risk Assessment: Instantly predict credit risk based on mixed tabular data.
2. 🏡 Real Estate Valuation: Zero-shot regression on property features for pricing.
3. 📉 Customer Churn: Identify subscription cancellations from usage data.
4. 🏥 Medical Triage: Classify patient risk levels from health records.
5. 📦 Inventory Forecasting: Predict stock needs using historical inputs.

🔮 5 Future Upgrades

1. 📁 Batch Upload: CSV support for bulk processing.
2. 🎛️ Model Selector: UI dropdown for various Ollama models.
3. 🖨️ PDF Export: Download AI insights instantly.
4. 📈 Dynamic Charts: Plotly integration for visual metrics.
5. ⚡ JAX Support: Toggle PyTorch/JAX backends directly in UI.

---

SEO Keywords: Google TabFM, Tabular Foundation Model, Local LLM, Ollama, Qwen3, Streamlit AI interface, zero-shot tabular predictions, offline AI inference, Python tabular analysis, data science automation.