#zero-shot ML

A graduate student created Zer0Fit, an MCP server that wraps Google's recently released TabFM and TimesFM foundation models, enabling zero-shot classification, regression, and time-series forecasting via a chat interface like Open WebUI, Claude Code, or Codex CLI. This lowers the barrier for using state-of-the-art zero-shot ML models, allowing developers and researchers to perform complex tabular and time-series tasks without training or tuning, directly from a local LLM interface. The server requires about 16GB of VRAM and runs on CUDA only (no Mac support), with dynamic model loading/unloading and a 5-minute TTL. It achieved 94.7% accuracy on the Iris dataset and an R2 of 0.91 on California housing regression.