Enhance model card with detailed information and usage for Kronos

This PR significantly enhances the model card for Kronos by integrating comprehensive details from its accompanying paper and GitHub repository. Key improvements include:
- A prominent title and introduction to the model's purpose and innovation.
- Direct links to the paper, the GitHub repository, and the live demo for easy access.
- Inclusion of the "Model Zoo" table, providing an overview and links to different model variants.
- Detailed "Getting Started" instructions with Python code examples for making forecasts.
- Proper citation information in BibTeX format.
- Relevant images from the project's GitHub to visually explain concepts and results.
- Removal of redundant "File information" section.

These additions aim to make the model more discoverable, understandable, and user-friendly for the Hugging Face community.

README.md

@@ -6,7 +6,159 @@ tags:
 - Candlestick
 - K-line
 ---
-# Model Card for Kronos
 
+# Kronos: A Foundation Model for the Language of Financial Markets
 
-For details on how to use this model, please visit our [GitHub page](https://github.com/shiyu-coder/Kronos).
+[![Paper](https://img.shields.io/badge/Paper-2508.02739-b31b1b.svg)](https://huggingface.co/papers/2508.02739)
+[![Live Demo](https://img.shields.io/badge/%F0%9F%9A%80-Live_Demo-brightgreen)](https://shiyu-coder.github.io/Kronos-demo/)
+[![GitHub](https://img.shields.io/badge/%F0%9F%92%BB-GitHub-blue?logo=github)](https://github.com/shiyu-coder/Kronos)
+
+<p align="center">
+  <img src="https://github.com/shiyu-coder/Kronos/raw/main/figures/logo.jpeg" alt="Kronos Logo" width="100">
+</p>
+
+**Kronos** is the **first open-source foundation model** for financial candlesticks (K-lines), trained on data from over **45 global exchanges**. It is designed to handle the unique, high-noise characteristics of financial data.
+
+## Introduction
+
+Kronos is a family of decoder-only foundation models, pre-trained specifically for the "language" of financial markets—K-line sequences. It leverages a novel two-stage framework:
+1.  A specialized tokenizer first quantizes continuous, multi-dimensional K-line data (OHLCV) into **hierarchical discrete tokens**.
+2.  A large, autoregressive Transformer is then pre-trained on these tokens, enabling it to serve as a unified model for diverse quantitative tasks.
+
+<p align="center">
+    <img src="https://github.com/shiyu-coder/Kronos/raw/main/figures/overview.png" alt="Kronos Overview" align="center" width="700px" />
+</p>
+
+The success of large-scale pre-training paradigm, exemplified by Large Language Models (LLMs), has inspired the development of Time Series Foundation Models (TSFMs). Kronos addresses existing limitations by introducing a specialized tokenizer that discretizes continuous market information into token sequences, preserving both price dynamics and trade activity patterns. We pre-train Kronos using an autoregressive objective on a massive, multi-market corpus of over 12 billion K-line records from 45 global exchanges, enabling it to learn nuanced temporal and cross-asset representations. Kronos excels in a zero-shot setting across a diverse set of financial tasks, including price series forecasting, volatility forecasting, and synthetic data generation.
+
+## Live Demo
+
+We have set up a live demo to visualize Kronos's forecasting results. The webpage showcases a forecast for the **BTC/USDT** trading pair over the next 24 hours.
+
+👉 [Access the Live Demo Here](https://shiyu-coder.github.io/Kronos-demo/)
+
+## Model Zoo
+
+We release a family of pre-trained models with varying capacities to suit different computational and application needs. All models are readily accessible from the Hugging Face Hub.
+
+| Model        | Tokenizer                                                                       | Context length | Param  | Hugging Face Model Card                                                  |
+|--------------|---------------------------------------------------------------------------------| -------------- | ------ |--------------------------------------------------------------------------|
+| Kronos-mini  | [Kronos-Tokenizer-2k](https://huggingface.co/NeoQuasar/Kronos-Tokenizer-2k)     | 2048           | 4.1M   | ✅ [NeoQuasar/Kronos-mini](https://huggingface.co/NeoQuasar/Kronos-mini)  |
+| Kronos-small | [Kronos-Tokenizer-base](https://huggingface.co/NeoQuasar/Kronos-Tokenizer-base) | 512            | 24.7M  | ✅ [NeoQuasar/Kronos-small](https://huggingface.co/NeoQuasar/Kronos-small) |
+| Kronos-base  | [Kronos-Tokenizer-base](https://huggingface.co/NeoQuasar/Kronos-Tokenizer-base) | 512            | 102.3M | ✅ [NeoQuasar/Kronos-base](https://huggingface.co/NeoQuasar/Kronos-base)   |
+| Kronos-large | [Kronos-Tokenizer-base](https://huggingface.co/NeoQuasar/Kronos-Tokenizer-base) | 512            | 499.2M | ❌ Not yet publicly available                                               |
+
+## Getting Started: Making Forecasts
+
+Forecasting with Kronos is straightforward using the `KronosPredictor` class. It handles data preprocessing, normalization, prediction, and inverse normalization, allowing you to get from raw data to forecasts in just a few lines of code.
+
+**Important Note**: The `max_context` for `Kronos-small` and `Kronos-base` is **512**. This is the maximum sequence length the model can process. For optimal performance, it is recommended that your input data length (i.e., `lookback`) does not exceed this limit. The `KronosPredictor` will automatically handle truncation for longer contexts.
+
+Here is a step-by-step guide to making your first forecast.
+
+### Installation
+
+1.  Install Python 3.10+, and then install the dependencies from the [GitHub repository's `requirements.txt`](https://github.com/shiyu-coder/Kronos/blob/main/requirements.txt):
+
+    ```shell
+    pip install -r requirements.txt
+    ```
+
+### 1. Load the Tokenizer and Model
+
+First, load a pre-trained Kronos model and its corresponding tokenizer from the Hugging Face Hub.
+
+```python
+from model import Kronos, KronosTokenizer, KronosPredictor
+
+# Load from Hugging Face Hub
+tokenizer = KronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-base")
+model = Kronos.from_pretrained("NeoQuasar/Kronos-small")
+```
+
+### 2. Instantiate the Predictor
+
+Create an instance of `KronosPredictor`, passing the model, tokenizer, and desired device.
+
+```python
+# Initialize the predictor
+predictor = KronosPredictor(model, tokenizer, device="cuda:0", max_context=512)
+```
+
+### 3. Prepare Input Data
+
+The `predict` method requires three main inputs:
+-   `df`: A pandas DataFrame containing the historical K-line data. It must include columns `['open', 'high', 'low', 'close']`. `volume` and `amount` are optional.
+-   `x_timestamp`: A pandas Series of timestamps corresponding to the historical data in `df`.
+-   `y_timestamp`: A pandas Series of timestamps for the future periods you want to predict.
+
+```python
+import pandas as pd
+
+# Load your data (example data can be found in the GitHub repo)
+df = pd.read_csv("./data/XSHG_5min_600977.csv")
+df['timestamps'] = pd.to_datetime(df['timestamps'])
+
+# Define context window and prediction length
+lookback = 400
+pred_len = 120
+
+# Prepare inputs for the predictor
+x_df = df.loc[:lookback-1, ['open', 'high', 'low', 'close', 'volume', 'amount']]
+x_timestamp = df.loc[:lookback-1, 'timestamps']
+y_timestamp = df.loc[lookback:lookback+pred_len-1, 'timestamps']
+```
+
+### 4. Generate Forecasts
+
+Call the `predict` method to generate forecasts. You can control the sampling process with parameters like `T`, `top_p`, and `sample_count` for probabilistic forecasting.
+
+```python
+# Generate predictions
+pred_df = predictor.predict(
+    df=x_df,
+    x_timestamp=x_timestamp,
+    y_timestamp=y_timestamp,
+    pred_len=pred_len,
+    T=1.0,          # Temperature for sampling
+    top_p=0.9,      # Nucleus sampling probability
+    sample_count=1  # Number of forecast paths to generate and average
+)
+
+print("Forecasted Data Head:")
+print(pred_df.head())
+```
+
+The `predict` method returns a pandas DataFrame containing the forecasted values for `open`, `high`, `low`, `close`, `volume`, and `amount`, indexed by the `y_timestamp` you provided.
+
+### 5. Example and Visualization
+
+For a complete, runnable script that includes data loading, prediction, and plotting, please see [`examples/prediction_example.py`](https://github.com/shiyu-coder/Kronos/blob/main/examples/prediction_example.py) in the GitHub repository.
+
+Running this script will generate a plot comparing the ground truth data against the model's forecast, similar to the one shown below:
+
+<p align="center">
+    <img src="https://github.com/shiyu-coder/Kronos/raw/main/figures/prediction_example.png" alt="Forecast Example" align="center" width="600px" />
+</p>
+
+Additionally, a script that makes predictions without Volume and Amount data can be found in [`examples/prediction_wo_vol_example.py`](https://github.com/shiyu-coder/Kronos/blob/main/examples/prediction_wo_vol_example.py).
+
+## Citation
+
+If you use Kronos in your research, we would appreciate a citation to our [paper](https://huggingface.co/papers/2508.02739):
+
+```bibtex
+@misc{shi2025kronos,
+      title={Kronos: A Foundation Model for the Language of Financial Markets}, 
+      author={Yu Shi and Zongliang Fu and Shuo Chen and Bohan Zhao and Wei Xu and Changshui Zhang and Jian Li},
+      year={2025},
+      eprint={2508.02739},
+      archivePrefix={arXiv},
+      primaryClass={q-fin.ST},
+      url={https://arxiv.org/abs/2508.02739}, 
+}
+```
+
+## License
+
+This project is licensed under the [MIT License](https://github.com/shiyu-coder/Kronos/blob/main/LICENSE).