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Student integrates Google's TabFM and TimesFM for local zero-shot ML tasks

By PulseAugur Editorial · [1 sources] ·

A graduate student has developed Zer0Fit, a local server that integrates Google's TabFM and TimesFM foundation models. This tool allows users to perform zero-shot machine learning tasks such as forecasting, classification, and regression by connecting to local LLM interfaces. Zer0Fit achieved notable accuracy scores, including 94.7% for the Iris dataset and an R2 of 0.91 for regression tasks, while requiring approximately 16GB of VRAM. AI

IMPACT Enables local, zero-shot execution of advanced ML tasks, potentially lowering the barrier for experimentation with foundation models.

RANK_REASON The item describes a user-created tool that integrates existing models, rather than a direct release from a frontier lab.

Read on r/MachineLearning →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Student integrates Google's TabFM and TimesFM for local zero-shot ML tasks

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The item describes a user-created tool that integrates existing models, rather than a direct release from a frontier lab.
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product, model release
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High
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46 days old
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COVERAGE [1]

  1. r/MachineLearning TIER_1 English(EN) · /u/Porespellar ·

    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]

    <table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1uue8cc/zer0fit_i_took_googles_new_tabfm_timesfm_ml/"> <img alt="Zer0Fit: I took Google's new TabFM &amp; TimesFM ML foundation models and made them available as an MCP server for zero-shot ML tasks (fore…

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