Introduction

zsfm-rs is a Rust workspace for running zero-shot forecasting and tabular foundation models locally, without Python. It has two halves — plus a cleanup helper:

1. Convert — download a model’s real weights from HuggingFace and turn them into a single self-contained GGUF file.
2. Infer — load that GGUF file and run zero-shot inference (a forecast, or a tabular classification/regression) via candle, Hugging Face’s Rust tensor library. No PyTorch, no Python runtime, one native binary.
3. Delete — remove a model’s cached model-f32.gguf + config.json (and optionally an output GGUF) to free disk (zsfm chronos delete).

Everything is exposed through one CLI binary, zsfm, with a subcommand per model:

zsfm chronos convert          # download + convert Chronos-2 to GGUF
zsfm chronos infer --gguf ... # run a forecast
zsfm chronos delete           # remove the cached files for Chronos-2

What’s included

11 time-series forecasters — point or quantile forecasts from a numeric context window:

Toto-2, Chronos-2, TimesFM 2.5, Sundial, TTM, Lag-Llama, MOMENT, Moirai 1.0, Moirai 2.0, FlowState-R1, TiRex.

5 tabular foundation models — zero-shot classification/regression from a small labeled support set, no fine-tuning:

Mitra, TabDPT, TabICL, TabPFN-3, TabFM.

See Time-series forecasters and Tabular foundation models for the exact request/response JSON for each.

Correctness

Every model in this workspace was verified bit-exact (or within pure F32 rounding, typically ~1e-6 to ~1e-7) against a reference Python implementation running the real downloaded checkpoint, before being considered done. This isn’t a from-scratch reimplementation guessing at architecture — each port was checked tensor-by-tensor against the original. Every model’s page links back to its original HuggingFace weights, the original authors’ source repo, and the paper it came from — this project converts and runs those exact weights, it doesn’t retrain or approximate them.

Where to go next

• New to the project? Start with Installation and Quick start.
• Want the exact JSON shapes for a specific model, or a link to its original repo/paper? Jump straight to Time-series forecasters or Tabular foundation models.
• Three of the 16 models (Moirai, TabPFN-3, TabFM) are non-commercial only — see Licensing before using them beyond research/internal evaluation.
• Looking for the generated Rust API reference (types, function signatures) rather than a usage guide? See the API docs.