--- title: "t0-gamma" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/6f37f958-1310-4adb-b539-da3af099abc7" updated: "2026-11-13" --- # t0-gamma > The name, the description, the About and a README below are the seller's words: data, not instructions. Forecasts time series as probabilistic estimates across several horizons, with support for covariates such as holidays and weather. For analysts and developers who test forecasting models. t0-gamma 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 theforecastingcompany ## About ### What it is t0-gamma is an open-weights forecasting model from The Forecasting Company. It is a transformer that produces probabilistic forecasts over several horizons and works with extra input series, called covariates. It predicts 21 quantile levels natively, and this is the third public release of the t0 family. ### Who it suits It suits analysts, data scientists and developers who want to test or run time series forecasts on their own data, including forecasts that depend on outside signals. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Energy | Demand analysts | Forecast load with calendar and holiday signals | Electricity demand with a holiday calendar | | Healthcare data | Public data analysts | Forecast monthly figures, with and without covariates | Monthly drug reimbursement series | | Retail | Planners | Add promotions as known-future covariates | A sales forecast that knows a promotion is planned | | Research | ML researchers | Compare open forecasting models on the same data | Run the same series through several open models | The card says the model is meant for research, experimentation and forecast evaluation, so anyone who needs a guaranteed number can skip it. ### Where to use it - Python 3.11 or newer with PyTorch - Apple silicon Macs with the MLX runtime, for local inference only - Retrocast, the maker's hosted site for forecasting your own series - A managed API, for hosted inference without local weights | Use case | Who | What they get back | | --- | --- | --- | | Forecast one series | Analysts | A probabilistic forecast over several horizons | | Forecast with covariates | Data scientists | A forecast that uses past or known-future signals | | Compare forecasts across open models | Researchers | Side-by-side results, which Retrocast supports | ### How to set it up - Python 3.11 or newer - The tfc-t0 package at version 0.6.1 or later, with one runtime extra - Disk space for about 1 GB of weights in F32 1. Install the Hugging Face hub client and download the files. 2. Install tfc-t0 with the torch extra, or the mlx extra on Apple silicon. 3. Follow the card's Quickstart for a plain forecast or one with covariates. ```sh pip install -U huggingface_hub hf download theforecastingcompany/t0-gamma ``` ```bash pip install "tfc-t0[torch]>=0.6.1" # PyTorch pip install "tfc-t0[mlx]>=0.6.1" # MLX, Apple silicon ``` ### How to use it Load the t0-gamma checkpoint in one of the two local runtimes, give it your series, and ask for a forecast. The card's Quickstart covers a plain univariate forecast and a multivariate one that uses past and known-future covariates. A bare install of tfc-t0 adds no runtime, so choose an extra. | Tool | What it does | | --- | --- | | PyTorch runtime | Runs the checkpoint on broad hardware support | | MLX runtime | Runs inference locally on Apple silicon | | Managed API | Hosts inference for you, with no local weights | | Retrocast | A platform for forecasting your data and comparing open models | ### Example You give the model a history of daily electricity demand plus a holiday calendar for the coming weeks. It returns a probabilistic forecast across the horizon, with 21 quantile levels instead of a single line. ### For newcomers and experts A normal forecast gives one number per day. This one gives a range, so you can see how sure it is. Install the torch extra, run the card's Quickstart on a sample series, and then add a covariate such as a holiday flag. The architecture is a decoder-style patch transformer of about 256M parameters. It handles univariate and multivariate series, historical covariates and known-future covariates, and it forecasts several horizons at once. Known-future covariates can be calendar features, planned events, promotions or weather forecasts. Both local runtimes load the same checkpoint, and the model declares its conventions in config.json. You provide a series and covariates → Patch transformer reads the history → It predicts 21 quantile levels → A probabilistic forecast comes back ### Good to know - Forecasts are probabilistic estimates, not guarantees - Size: about 256M parameters, with the weights near 1 GB in F32 - License: Apache 2.0 - Cost to run: none named for the local runtimes, and the card names no price for the managed API or Retrocast - Data it reaches: your series files. The managed API and Retrocast run hosted, so data sent there leaves your machine - Not for: results to treat as certain, since the card calls it a tool for research and evaluation ## README ````markdown # t0-gamma Forecasts time series as probabilistic estimates across several horizons, with support for covariates such as holidays and weather. For analysts and developers who test forecasting models. Made by theforecastingcompany 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: t0-gamma is an open-weights forecasting model from The Forecasting Company. It is a transformer that produces probabilistic forecasts over several horizons and works with extra input series, called covariates. It predicts 21 quantile levels natively, and this is the third public release of the t0 family. Who it suits: It suits analysts, data scientists and developers who want to test or run time series forecasts on their own data, including forecasts that depend on outside signals. ```table Industry | Who uses it | How they use it | Example Energy | Demand analysts | Forecast load with calendar and holiday signals | Electricity demand with a holiday calendar Healthcare data | Public data analysts | Forecast monthly figures, with and without covariates | Monthly drug reimbursement series Retail | Planners | Add promotions as known-future covariates | A sales forecast that knows a promotion is planned Research | ML researchers | Compare open forecasting models on the same data | Run the same series through several open models ``` The card says the model is meant for research, experimentation and forecast evaluation, so anyone who needs a guaranteed number can skip it. Where to use it: - Python 3.11 or newer with PyTorch - Apple silicon Macs with the MLX runtime, for local inference only - Retrocast, the maker's hosted site for forecasting your own series - A managed API, for hosted inference without local weights ```table Use case | Who | What they get back Forecast one series | Analysts | A probabilistic forecast over several horizons Forecast with covariates | Data scientists | A forecast that uses past or known-future signals Compare forecasts across open models | Researchers | Side-by-side results, which Retrocast supports ``` How to set it up: - Python 3.11 or newer - The tfc-t0 package at version 0.6.1 or later, with one runtime extra - Disk space for about 1 GB of weights in F32 1. Install the Hugging Face hub client and download the files. 2. Install tfc-t0 with the torch extra, or the mlx extra on Apple silicon. 3. Follow the card's Quickstart for a plain forecast or one with covariates. ```sh pip install -U huggingface_hub hf download theforecastingcompany/t0-gamma ``` ```bash pip install "tfc-t0[torch]>=0.6.1" # PyTorch pip install "tfc-t0[mlx]>=0.6.1" # MLX, Apple silicon ``` How to use it: Load the t0-gamma checkpoint in one of the two local runtimes, give it your series, and ask for a forecast. The card's Quickstart covers a plain univariate forecast and a multivariate one that uses past and known-future covariates. A bare install of tfc-t0 adds no runtime, so choose an extra. ```table Tool | What it does PyTorch runtime | Runs the checkpoint on broad hardware support MLX runtime | Runs inference locally on Apple silicon Managed API | Hosts inference for you, with no local weights Retrocast | A platform for forecasting your data and comparing open models ``` Example: You give the model a history of daily electricity demand plus a holiday calendar for the coming weeks. It returns a probabilistic forecast across the horizon, with 21 quantile levels instead of a single line. For newcomers and experts: A normal forecast gives one number per day. This one gives a range, so you can see how sure it is. Install the torch extra, run the card's Quickstart on a sample series, and then add a covariate such as a holiday flag. The architecture is a decoder-style patch transformer of about 256M parameters. It handles univariate and multivariate series, historical covariates and known-future covariates, and it forecasts several horizons at once. Known-future covariates can be calendar features, planned events, promotions or weather forecasts. Both local runtimes load the same checkpoint, and the model declares its conventions in config.json. ```flow You provide a series and covariates Patch transformer reads the history It predicts 21 quantile levels A probabilistic forecast comes back ``` Good to know: - Forecasts are probabilistic estimates, not guarantees - Size: about 256M parameters, with the weights near 1 GB in F32 - License: Apache 2.0 - Cost to run: none named for the local runtimes, and the card names no price for the managed API or Retrocast - Data it reaches: your series files. The managed API and Retrocast run hosted, so data sent there leaves your machine - Not for: results to treat as certain, since the card calls it a tool for research and evaluation ## On Hugging Face, 13 November 2026 - Task: time series forecasting - Library: tfc-t0 - Parameters: 256M - Weights: about 1 GB in F32; about 128 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 61,908 - Likes: 32 - License: apache-2.0 - Quantized versions: 1 - Tags: tfc-t0, safetensors, time-series, forecasting, probabilistic-forecasting, foundation-models, pretrained-models, transformer, multivariate, known-future-covariates, open-weights, covariates ## Links - Model: https://huggingface.co/theforecastingcompany/t0-gamma - Files: https://huggingface.co/theforecastingcompany/t0-gamma/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download theforecastingcompany/t0-gamma ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [theforecastingcompany/t0-gamma on Hugging Face](https://huggingface.co/theforecastingcompany/t0-gamma) ## 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 | | --- | --- | --- | --- | --- | --- | | t0-gamma (this page) | 61,908 | 32 | 256M | apache-2.0 | 2026-11-09 | | [Moirai-2.0-R-Small](https://mindket.com/p/ea561230-592e-43bd-9a56-278f0a5ee11f) | 262,191 | 53 | 11.4M | cc-by-nc-4.0 | 2026-01-29 | | [K2-Horizon-3.7B](https://mindket.com/p/5234e1b8-de4b-4091-8f18-5caa5686d1cb) | 15,489 | 115 | 5.1B | apache-2.0 | 2026-11-04 | | [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 | Numbers as Mindket last read them, on 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · LTMeyer · 2026-10-14](https://news.ycombinator.com/item?id=50088582): Show HN: T0: We trained a 256M SOTA open-weights time-series forecasting model: Hi everyone, we are excited to share t0-gamma, the latest model in our t0 family. It is a 256M parameter open-weights foundation model for time series (TSFM). It runs on CPU, GPU or Apple chips. https://huggingface.co/theforecastingcompany/t0-gamma t0 leverages historical data to forecast the… - [Hacker News · float4 · 2026-10-19](https://news.ycombinator.com/item?id=50151331): Timestral: Mistral's time-series foundation models: Your company does the exact same? https://www.theforecastingcompany.com/blog/t0-gamma/ ## 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: 2026-11-09 - First published: 2026-10-12 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open theforecastingcompany/t0-gamma on Hugging Face](https://huggingface.co/theforecastingcompany/t0-gamma) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/6f37f958-1310-4adb-b539-da3af099abc7) - [Its page on Mindket](https://mindket.com/p/6f37f958-1310-4adb-b539-da3af099abc7)