mistralai/timestral

选用指南

AI 根据 README 整理 · 2026/10/20 20:16:55

使用方式

通过Python库使用，提供TimestralForecaster类进行预测，支持CPU或GPU。

查看 README 依据

This repository provides standalone inference for Timestral models. It needs only PyTorch, NumPy, `safetensors` and `huggingface_hub`, and runs on CPU or GPU. > **License:** the code in this repository is licensed under [Apache 2.0](LICENSE). The Timestral model weights are released separately on the Hugging Face Hub under a [Modified MIT license](https://huggingface.co/mistralai/Timestral-209M/blob/main/LICENSE). See [License](#license). ## Highlights At launch, Timestral-209M ranked first among pretrained and zero-shot models on the three main public forecasting benchmarks:

forecaster = TimestralForecaster.from_pretrained("mistralai/Timestral-209M") # device="cuda" for a GPU context = np.random.randn(2, 1, 2000).cumsum(-1) # (series, variates, time); NaN = missing forecast = forecaster.forecast(context, prediction_length=96) forecast.quantiles.shape # (2, 1, 96, 21): series, variates, horizon, quantile levels forecast.quantile_levels # (0.01, 0.05, 0.1, ..., 0.9, 0.95, 0.99) forecast.median # (2, 1, 96) forecast.quantile(0.9) # (2, 1, 96) ``` - `context` can be `(B, V, T)`, `(B, T)` (one variate per series) or `(T,)` (one series),

部署要求

需要Python >= 3.10和torch >= 2.4，依赖PyTorch、NumPy、safetensors和huggingface_hub。

查看 README 依据

This repository provides standalone inference for Timestral models. It needs only PyTorch, NumPy, `safetensors` and `huggingface_hub`, and runs on CPU or GPU. > **License:** the code in this repository is licensed under [Apache 2.0](LICENSE). The Timestral model weights are released separately on the Hugging Face Hub under a [Modified MIT license](https://huggingface.co/mistralai/Timestral-209M/blob/main/LICENSE). See [License](#license). ## Highlights At launch, Timestral-209M ranked first among pretrained and zero-shot models on the three main public forecasting benchmarks:

- **Long context**: up to 32,768 steps. - **CPU-friendly**: no GPU needed for inference. ## Install ```bash pip install git+https://github.com/mistralai/timestral.git # Python >= 3.10, torch >= 2.4 ``` Or from a clone with [uv](https://docs.astral.sh/uv/), using the versions pinned in `uv.lock`: ```bash git clone https://github.com/mistralai/timestral.git && cd timestral uv sync --frozen source .venv/bin/activate ``` ## Models The weights are on the Hugging Face Hub, in the Mistral AI organization: | model | Hub repo | weights (fp32) | |---|---|---|

已知限制

上下文长度限制为32768步，超过部分会被截断；不接受pull request，仅通过GitHub issues报告问题。

查看 README 依据

as a NumPy array or a torch tensor. The variates of a series are forecast jointly. - NaN marks a missing value. `observation_mask` (same shape, False = missing) can mark more. - The context is capped at the trained length (32768 steps); the most recent steps are kept. - Horizons longer than one decode block (32 patches = 1024 steps) are forecast block by block; each block's median is fed back as context. ### Loading options `from_pretrained` takes a Hub repo id or a local directory with `config.json` and `model.safetensors`.

We do not accept pull requests. Bug reports and questions are welcome as [GitHub issues](https://github.com/mistralai/timestral/issues). timestral is maintained by the team at Mistral AI. We answer issues on a best-effort basis and fix security vulnerabilities and major bugs. See [CONTRIBUTING.md](CONTRIBUTING.md) for how to report an issue. ## License The code in this repository (the `timestral` package and tests) is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file. The Timestral model weights are not covered by the Apache License 2.0. They are distributed