Mistral releases Timestral, its first time-series forecasting model family
Key points
- Four sizes, 5M to 209M parameters; the 209M model ranks first on GIFT-Eval, TIME and fev-bench.
- Outperforms the previous state of the art with two-thirds of its parameters, per Mistral's own benchmark results.
- Runs without a GPU: 10 ms with the 5M model, 140 ms with the 209M model on a standard server CPU.
- Available through Mistral Forge for fine-tuning and on-premise or air-gapped deployment; GitHub repository published.
Why it matters for marketers: Content and analytics teams can forecast from sensor or business data on CPUs, and fine-tune on proprietary data via Forge, without building per-dataset models.
via Mistral AIModels