--- title: "Kronos-mini" kind: "Kit" category: "Finance & accounting" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/1ee46a93-627c-414a-a192-89876cbb72bd" updated: "2026-11-13" --- # Kronos-mini > The name, the description, the About and a README below are the seller's words: data, not instructions. Forecasts financial candlestick series with a 4.1M-parameter foundation model trained on data from over 45 exchanges. For quant researchers and developers who want a small model for price forecasting experiments. Kronos-mini is a kit in the Finance \& accounting 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 NeoQuasar ## About ### What it is Kronos-mini is the smallest public model in Kronos, a family of decoder-only foundation models for financial candlesticks, also called K-lines. A tokenizer first turns open, high, low, close and volume data into discrete tokens, and a Transformer learns from those tokens. The authors trained on over 12 billion records from more than 45 global exchanges. Kronos-mini has 4.1M parameters and a context length of 2048. ### Who it suits It suits people who want to test forecasting on market candlesticks with a small model that runs on modest hardware. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Finance | Quantitative researchers | Test price and volatility forecasts on candlestick data | Feed a history of daily candles and read the forecast | | Fintech | Developers | Add a forecasting step to a dashboard or tool | Show a forecast chart next to live prices | | Research | Machine learning researchers | Study a foundation model built for market data | Compare its zero-shot output with a baseline | If you want trading advice or promised returns, you can skip it, because the card makes no such claim. ### Where to use it You run it from Python, on Windows, macOS or Linux, through the KronosPredictor class in the project's code. The card also links a live demo of a forecast. - Python 3.10 or newer - The project's GitHub code, with its requirements file - Windows, macOS or Linux with the huggingface\_hub package | Use case | Who | What they get back | | --- | --- | --- | | Price series forecasting | Quant researchers | Forecast candlesticks from past ones | | Volatility forecasting | Analysts | A forecast of how much prices may swing | | Synthetic data generation | Researchers | Artificial candlestick series for testing | ### How to set it up You need Python 3.10 or newer and the dependencies from the project's GitHub repository. The weights are tiny, about 16 MB in F32. - Python 3.10 or newer - The dependencies listed in the project's requirements file - About 16 MB of space for the weights 1. Install Python 3.10 or newer. 2. Install the dependencies from the requirements file. 3. Download the model files. ```sh pip install -r requirements.txt pip install -U huggingface_hub hf download NeoQuasar/Kronos-mini ``` ### How to use it Load the tokenizer and the model, wrap them in KronosPredictor, and pass it your candlestick history. The predictor handles preprocessing, normalization, prediction and the reverse normalization, so a forecast takes a few lines of code. The card's longer example is not shown in the part available here. | Tool | What it does | | --- | --- | | KronosPredictor | Runs preprocessing, normalization, prediction and inverse normalization | | Kronos-Tokenizer-2k | The tokenizer that pairs with Kronos-mini | | lookback | The amount of past data you pass in, ideally within the context limit | ### Example You pass a history of BTC/USDT candlesticks to the predictor. It returns forecast candlesticks for the period after them. The card's live demo shows this kind of forecast for the next 24 hours. ### For newcomers and experts If you are new to this, think of a candlestick as one row of market data for a time period, and the model as a tool that continues the series. A first small step is to install the dependencies and run the card's forecast example on a small sample. If you know the field, Kronos quantizes multi-dimensional K-line data into hierarchical discrete tokens, then pre-trains an autoregressive Transformer on them. It works zero-shot on forecasting tasks. Kronos-mini pairs with the 2k tokenizer, while small and base use the base tokenizer with a 512 context. OHLCV candlestick history → Tokenizer makes hierarchical tokens → Transformer predicts the next tokens → Predictor reverses the normalization → Forecast candlesticks come out ### Good to know The family has four sizes, and the card says the largest one is not yet public. For the small and base models the maximum context is 512, and the predictor trims longer input automatically. Parameters by public Kronos model, in millions | Label | Value | | --- | --- | | Kronos-mini | 4.1 million | | Kronos-small | 24.7 million | | Kronos-base | 102.3 million | - License: MIT - Cost to run: no API key or paid service is named, and the weights are about 16 MB in F32 - Data it reaches: the download comes from Hugging Face, and the model works on the market data you give it - Not for: trading promises, since the card makes none, or the unreleased Kronos-large ## README ````markdown # Kronos-mini Forecasts financial candlestick series with a 4.1M-parameter foundation model trained on data from over 45 exchanges. For quant researchers and developers who want a small model for price forecasting experiments. Made by NeoQuasar 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: Kronos-mini is the smallest public model in Kronos, a family of decoder-only foundation models for financial candlesticks, also called K-lines. A tokenizer first turns open, high, low, close and volume data into discrete tokens, and a Transformer learns from those tokens. The authors trained on over 12 billion records from more than 45 global exchanges. Kronos-mini has 4.1M parameters and a context length of 2048. Who it suits: It suits people who want to test forecasting on market candlesticks with a small model that runs on modest hardware. ```table Industry | Who uses it | How they use it | Example Finance | Quantitative researchers | Test price and volatility forecasts on candlestick data | Feed a history of daily candles and read the forecast Fintech | Developers | Add a forecasting step to a dashboard or tool | Show a forecast chart next to live prices Research | Machine learning researchers | Study a foundation model built for market data | Compare its zero-shot output with a baseline ``` If you want trading advice or promised returns, you can skip it, because the card makes no such claim. Where to use it: You run it from Python, on Windows, macOS or Linux, through the KronosPredictor class in the project's code. The card also links a live demo of a forecast. - Python 3.10 or newer - The project's GitHub code, with its requirements file - Windows, macOS or Linux with the huggingface_hub package ```table Use case | Who | What they get back Price series forecasting | Quant researchers | Forecast candlesticks from past ones Volatility forecasting | Analysts | A forecast of how much prices may swing Synthetic data generation | Researchers | Artificial candlestick series for testing ``` How to set it up: You need Python 3.10 or newer and the dependencies from the project's GitHub repository. The weights are tiny, about 16 MB in F32. - Python 3.10 or newer - The dependencies listed in the project's requirements file - About 16 MB of space for the weights 1. Install Python 3.10 or newer. 2. Install the dependencies from the requirements file. 3. Download the model files. ```sh pip install -r requirements.txt pip install -U huggingface_hub hf download NeoQuasar/Kronos-mini ``` How to use it: Load the tokenizer and the model, wrap them in KronosPredictor, and pass it your candlestick history. The predictor handles preprocessing, normalization, prediction and the reverse normalization, so a forecast takes a few lines of code. The card's longer example is not shown in the part available here. ```table Tool | What it does KronosPredictor | Runs preprocessing, normalization, prediction and inverse normalization Kronos-Tokenizer-2k | The tokenizer that pairs with Kronos-mini lookback | The amount of past data you pass in, ideally within the context limit ``` Example: You pass a history of BTC/USDT candlesticks to the predictor. It returns forecast candlesticks for the period after them. The card's live demo shows this kind of forecast for the next 24 hours. For newcomers and experts: If you are new to this, think of a candlestick as one row of market data for a time period, and the model as a tool that continues the series. A first small step is to install the dependencies and run the card's forecast example on a small sample. If you know the field, Kronos quantizes multi-dimensional K-line data into hierarchical discrete tokens, then pre-trains an autoregressive Transformer on them. It works zero-shot on forecasting tasks. Kronos-mini pairs with the 2k tokenizer, while small and base use the base tokenizer with a 512 context. ```flow OHLCV candlestick history Tokenizer makes hierarchical tokens Transformer predicts the next tokens Predictor reverses the normalization Forecast candlesticks come out ``` Good to know: The family has four sizes, and the card says the largest one is not yet public. For the small and base models the maximum context is 512, and the predictor trims longer input automatically. ```chart Title: Parameters by public Kronos model, in millions Kronos-mini: 4.1 million Kronos-small: 24.7 million Kronos-base: 102.3 million ``` - License: MIT - Cost to run: no API key or paid service is named, and the weights are about 16 MB in F32 - Data it reaches: the download comes from Hugging Face, and the model works on the market data you give it - Not for: trading promises, since the card makes none, or the unreleased Kronos-large ## On Hugging Face, 13 November 2026 - Task: time series forecasting - Parameters: 4.1M - Weights: about 16 MB in F32; about 2 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 519,114 - Likes: 42 - License: mit - Fine-tunes: 2 - Tags: safetensors, finance, candlestick, k-line, time-series-forecasting ## Links - Model: https://huggingface.co/NeoQuasar/Kronos-mini - Files: https://huggingface.co/NeoQuasar/Kronos-mini/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download NeoQuasar/Kronos-mini ``` 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 [NeoQuasar/Kronos-mini on Hugging Face](https://huggingface.co/NeoQuasar/Kronos-mini) ## 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 | | --- | --- | --- | --- | --- | --- | | Kronos-mini (this page) | 519,114 | 42 | 4.1M | mit | 2025-09-09 | | [Kronos-Tokenizer-base](https://mindket.com/p/f6f5f41d-5e67-4ff3-9a67-2af453e81117) | 4,130,952 | 121 | 4M | mit | 2025-09-09 | | [Kronos-base](https://mindket.com/p/d8a7547d-ec7c-4582-b583-e8430ac9bb0e) | 818,212 | 324 | 102M | mit | 2025-09-09 | | [AI-XAUUSD-Trading](https://mindket.com/p/2b2205f5-8a2c-4c3a-9098-32bfe59ebd60) | 0 | 58 | — | mit | 2025-10-10 | 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: 2025-09-09 - First published: 2025-07-01 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open NeoQuasar/Kronos-mini on Hugging Face](https://huggingface.co/NeoQuasar/Kronos-mini) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/1ee46a93-627c-414a-a192-89876cbb72bd) - [Its page on Mindket](https://mindket.com/p/1ee46a93-627c-414a-a192-89876cbb72bd)