--- title: "Moirai-2.0-R-Small" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/ea561230-592e-43bd-9a56-278f0a5ee11f" updated: "2026-11-13" --- # Moirai-2.0-R-Small > The name, the description, the About and a README below are the seller's words: data, not instructions. Forecasts future values of a time series with a small decoder-only transformer trained on many kinds of series. A Salesforce research model for analysts and data scientists. Moirai-2.0-R-Small is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Salesforce ## About ### What it is Moirai 2.0 R Small is a time series forecasting model from Salesforce with 11.4 million parameters. It is a decoder-only transformer meant to forecast many kinds of series without per-dataset training. Salesforce released it for research purposes only. ### Who it suits It suits analysts, data scientists and researchers who forecast numbers over time and want a small pretrained model to try. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Retail | Demand analysts | Forecast a sales series from its past values | Project the next weeks of daily orders | | Energy | Grid and load analysts | Forecast usage over time | Project hourly demand | | Research | Time series researchers | Benchmark a small pretrained forecaster | Compare it with older models on public data | Anyone who needs a model for business use or for high-risk decisions can skip it, since the release is for research only. ### Where to use it - Python, through the uni2ts library from Salesforce - A Jupyter notebook, since the library ships a simple example - A shell where you can make a virtual environment - Windows, macOS or Linux with Python, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Forecast a series | Data scientists | Predicted values for the steps that follow | | Series with gaps | Analysts | A model whose patch embedding carries missing-value information | | Research comparison | Researchers | A small 11.4M parameter reference to test against | ### How to set it up You need a few things first. - Python and pip - Git, to clone the uni2ts repository from Salesforce's GitHub - The virtualenv tool 1. Clone the uni2ts repository, then open its folder. 2. Create and activate a virtual environment. 3. Build the library from source with the notebook extras. 4. Create an empty .env file. ```sh cd uni2ts virtualenv venv . venv/bin/activate pip install -e '.[notebook]' touch .env ``` To fetch the model files on their own, use the Hugging Face tool. ```sh pip install -U huggingface_hub hf download Salesforce/moirai-2.0-R-small ``` ### How to use it The quickest start is the simple example notebook in the uni2ts repository, which runs a forecast with Moirai. You open it in Jupyter and follow its cells with your own series. | Tool | What it does | | --- | --- | | uni2ts | The Salesforce library that runs Moirai 2.0 inference | | moirai\_forecast.ipynb | The simple example notebook for getting started | | .env file | A file you create in setup, which the library expects | | hf download | Fetches the model files from Hugging Face | ### Example You open the example notebook with a series of past values, such as daily sales. Moirai 2.0 reads that history and returns a forecast for the steps that follow, which you can plot next to the real values. ### For newcomers and experts A forecasting model looks at numbers recorded over time and estimates what comes next. For a first try, set up uni2ts, open the example notebook and run it as it is before swapping in your own data. Compared with the first Moirai, this version uses a quantile loss instead of a distributional one, and predicts several tokens at once for better efficiency and stability. It filters out series that cannot be forecast, adds missing-value information to the patch embedding, and masks random patches so inference holds up better. Pretraining mixed GIFT-Eval data, Chronos-derived data, KernelSynth series and internal Salesforce operational data. Past values are cut into patch tokens → Patches carry missing-value information → Decoder-only transformer reads the history → Several future tokens predicted at once → Forecast is returned ### Good to know - License: cc-by-nc-4.0, a Creative Commons licence for non-commercial use. - Cost to run: no API key or paid service named. The model is about 46 MB in F32 by the record. - Data it reaches: the series you give it. Setup clones a repository and installs packages over the network. - Not for: business use or high-risk decisions. The card asks you to check accuracy, safety and fairness before deploying anything built on it. ## README ````markdown # Moirai-2.0-R-Small Forecasts future values of a time series with a small decoder-only transformer trained on many kinds of series. A Salesforce research model for analysts and data scientists. Made by Salesforce on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Moirai 2.0 R Small is a time series forecasting model from Salesforce with 11.4 million parameters. It is a decoder-only transformer meant to forecast many kinds of series without per-dataset training. Salesforce released it for research purposes only. Who it suits: It suits analysts, data scientists and researchers who forecast numbers over time and want a small pretrained model to try. ```table Industry | Who uses it | How they use it | Example Retail | Demand analysts | Forecast a sales series from its past values | Project the next weeks of daily orders Energy | Grid and load analysts | Forecast usage over time | Project hourly demand Research | Time series researchers | Benchmark a small pretrained forecaster | Compare it with older models on public data ``` Anyone who needs a model for business use or for high-risk decisions can skip it, since the release is for research only. Where to use it: - Python, through the uni2ts library from Salesforce - A Jupyter notebook, since the library ships a simple example - A shell where you can make a virtual environment - Windows, macOS or Linux with Python, for downloading the files ```table Use case | Who | What they get back Forecast a series | Data scientists | Predicted values for the steps that follow Series with gaps | Analysts | A model whose patch embedding carries missing-value information Research comparison | Researchers | A small 11.4M parameter reference to test against ``` How to set it up: You need a few things first. - Python and pip - Git, to clone the uni2ts repository from Salesforce's GitHub - The virtualenv tool 1. Clone the uni2ts repository, then open its folder. 2. Create and activate a virtual environment. 3. Build the library from source with the notebook extras. 4. Create an empty .env file. ```sh cd uni2ts virtualenv venv . venv/bin/activate pip install -e '.[notebook]' touch .env ``` To fetch the model files on their own, use the Hugging Face tool. ```sh pip install -U huggingface_hub hf download Salesforce/moirai-2.0-R-small ``` How to use it: The quickest start is the simple example notebook in the uni2ts repository, which runs a forecast with Moirai. You open it in Jupyter and follow its cells with your own series. ```table Tool | What it does uni2ts | The Salesforce library that runs Moirai 2.0 inference moirai_forecast.ipynb | The simple example notebook for getting started .env file | A file you create in setup, which the library expects hf download | Fetches the model files from Hugging Face ``` Example: You open the example notebook with a series of past values, such as daily sales. Moirai 2.0 reads that history and returns a forecast for the steps that follow, which you can plot next to the real values. For newcomers and experts: A forecasting model looks at numbers recorded over time and estimates what comes next. For a first try, set up uni2ts, open the example notebook and run it as it is before swapping in your own data. Compared with the first Moirai, this version uses a quantile loss instead of a distributional one, and predicts several tokens at once for better efficiency and stability. It filters out series that cannot be forecast, adds missing-value information to the patch embedding, and masks random patches so inference holds up better. Pretraining mixed GIFT-Eval data, Chronos-derived data, KernelSynth series and internal Salesforce operational data. ```flow Past values are cut into patch tokens Patches carry missing-value information Decoder-only transformer reads the history Several future tokens predicted at once Forecast is returned ``` Good to know: - License: cc-by-nc-4.0, a Creative Commons licence for non-commercial use. - Cost to run: no API key or paid service named. The model is about 46 MB in F32 by the record. - Data it reaches: the series you give it. Setup clones a repository and installs packages over the network. - Not for: business use or high-risk decisions. The card asks you to check accuracy, safety and fairness before deploying anything built on it. ## On Hugging Face, 13 November 2026 - Task: time series forecasting - Parameters: 11.4M - Weights: about 46 MB in F32; about 6 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 262,191 - Likes: 53 - License: cc-by-nc-4.0 - Quantized versions: 1 - Fine-tunes: 1 - Tags: safetensors, forecasting, time-series, time-series-forecasting ## Links - Model: https://huggingface.co/Salesforce/moirai-2.0-R-small - Files: https://huggingface.co/Salesforce/moirai-2.0-R-small/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Salesforce/moirai-2.0-R-small ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [Salesforce/moirai-2.0-R-small on Hugging Face](https://huggingface.co/Salesforce/moirai-2.0-R-small) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Moirai-2.0-R-Small (this page) | 262,191 | 53 | 11.4M | cc-by-nc-4.0 | 2026-01-29 | | [t0-gamma](https://mindket.com/p/6f37f958-1310-4adb-b539-da3af099abc7) | 61,908 | 32 | 256M | apache-2.0 | 2026-11-09 | | [IMUDINO](https://mindket.com/p/401955b5-05c7-41f4-924d-9944c164bc4c) | 0 | 3 | — | gpl-3.0 | 2026-11-06 | | [DeepSeek-V4-Pro](https://mindket.com/p/02f743ba-81c7-498f-88ea-98edbc4b5d2b) | 322,162 | 5,702 | 1.6T | mit | 2026-06-22 | | [VLX-Seek-1.5-10B](https://mindket.com/p/4b1edf89-b48a-4dfc-b86e-a0ee020dc119) | 137,772 | 39 | 10B | apache-2.0 | 2026-07-23 | | [Hy-Nomas](https://mindket.com/p/9a697cce-2966-4f2d-bb74-d8d6874fbecb) | 90,896 | 153 | — | apache-2.0 | 2026-11-04 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-01-29 - First published: 2025-08-06 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Salesforce/moirai-2.0-R-small on Hugging Face](https://huggingface.co/Salesforce/moirai-2.0-R-small) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/ea561230-592e-43bd-9a56-278f0a5ee11f) - [Its page on Mindket](https://mindket.com/p/ea561230-592e-43bd-9a56-278f0a5ee11f)