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Artificial intelligence

Mistral releases small forecasting models that can run on a CPU

Timestral brings downloadable, probabilistic time-series forecasting to CPU-sized models, with separate terms for the code and weights.

141 words · 1 min

Mistral’s October 15 Timestral release comprises four models, from five million to 209 million parameters.

They forecast numerical sequences rather than generate prose and can run through PyTorch on a CPU or GPU. [1][2]

The models accept multiple related series and additional variables, including information known about the future, such as a planned schedule. They return 21 forecast quantiles rather than only one predicted line. That is useful for testing decisions against a range of outcomes, provided the forecasts are calibrated on the reader’s own data. [2][3]

The code is Apache 2.0. The weights’ Modified MIT licence excludes use under that licence if the company or employer’s global consolidated revenue exceeded $20 million in the preceding month; a separate commercial licence is an option. Before adoption, check those terms and compare forecasts with a simple baseline on held-out periods. Launch benchmarks remain vendor-reported. [1][2][4]

Take it further

Inspect the model and weight terms The model card links installation and weight terms. The weight licence has a $20 million preceding-month company-revenue threshold; review eligibility before use.

Sources & details 4 sources
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Reporting sources

  1. Mistral · Timestral launch

    October 15 release announcement and vendor benchmark claims.

  2. Mistral · Source code and examples

    Original repository documents CPU/GPU inference, covariates, sizes and Apache 2.0 code licensing.

  3. Mistral · 209M model card

    Original model card specifies multivariate inputs, 21 quantiles and numerical forecast outputs.

  4. Mistral · Weight licence

    Full licence opened October 16: Modified MIT terms include a restriction tied to consolidated company revenue. Code and weight licences are distinct.

Images & figures

  • AI-generated editorial illustration of observed history giving rise to uncertain forecasts, with known future context; not documentary evidence or a measured diagram.

    Euangelion · AI-generated editorial illustration · Source · Usage terms