Zer0Fit: I took Google's new TabFM & TimesFM ML foundation models and made them available as an MCP server for zero-shot ML tasks (forecasts / classifications / regressions). 100% local. [P]

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Summary

Zer0Fit provides an MCP server that wraps Google's TabFM and TimesFM foundation models for zero-shot forecasting, classification, and regression tasks, running entirely locally.

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TabFM: A zero-shot foundation model for tabular data

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Google Research introduces TabFM, a zero-shot foundation model for tabular data that uses in-context learning to perform classification and regression without requiring manual model training or hyperparameter tuning.

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Google Research releases TabFM, a zero-shot tabular foundation model for classification and regression using PyTorch, requiring no fine-tuning.

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Google Research releases TimesFM 2.5, an open-source time-series foundation model for forecasting, with 200M parameters, 16k context length, and support for quantile forecasts up to 1k horizon. The model is available on PyPI and Hugging Face, with fine-tuning via LoRA and integration into Google products like BigQuery ML, Google Sheets, and Vertex Model Garden.