Update README.md

Update Readme to match the one on github.

README.md

@@ -9,58 +9,64 @@ license_link: https://huggingface.co/NX-AI/TiRex/blob/main/LICENSE
 license_name: nx-ai-community-license
 ---
 
-# TiRex
+<div align="center">
 
-TiRex is a **time-series foundation model** designed for **time series forecasting**,
-with the emphasis to provide state-of-the-art forecasts for both short- and long-term forecasting horizon.
-TiRex is **35M parameter** small and is based on the **[xLSTM architecture](https://github.com/NX-AI/xlstm)** allowing fast and performant forecasts.
-The model is described in the paper [TiRex: Zero-Shot Forecasting across Long and Short Horizons with Enhanced In-Context Learning](https://arxiv.org/abs/2505.23719).
+# TiRex: Zero-Shot Forecasting across Long and Short Horizons
+
+[![Paper](https://img.shields.io/static/v1?label=Paper&message=2505.23719&color=B31B1B&logo=arXiv)](https://arxiv.org/abs/2505.23719)
+[![Hugging Face](https://img.shields.io/badge/HuggingFace-TiRex-yellow?logo=huggingface)](https://huggingface.co/NX-AI/TiRex)
+[![PyPI](https://img.shields.io/pypi/v/tirex-ts?color=blue)](https://pypi.org/project/tirex-ts/)
+[![PyPI Downloads](https://static.pepy.tech/personalized-badge/tirex-ts?period=total&units=INTERNATIONAL_SYSTEM&left_color=GREY&right_color=BLUE&left_text=downloads)](https://pepy.tech/projects/tirex-ts)
+[![License](https://img.shields.io/badge/license-nxai_community-green)](https://github.com/NX-AI/tirex-internal/blob/main/LICENSE)
+[![docs](https://img.shields.io/website-up-down-green-red/http/nx-ai.github.io/tirex//.svg?label=docs)](https://nx-ai.github.io/tirex/)
+[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit)](https://github.com/pre-commit/pre-commit)
+[![formatter: docformatter](https://img.shields.io/badge/formatter-docformatter-fedcba.svg)](https://github.com/PyCQA/docformatter)
+
+</div>
+
+
+This repository provides the pre-trained forecasting model TiRex introduced in the paper
+[TiRex: Zero-Shot Forecasting across Long and Short Horizons with Enhanced In-Context Learning](https://arxiv.org/abs/2505.23719).
+
+
+## TiRex Model
+
+TiRex is a 35M parameter pre-trained time series forecasting model based on [xLSTM](https://github.com/NX-AI/xlstm).
 
 ### Key Facts:
 
 - **Zero-Shot Forecasting**:
-  TiRex performs forecasting without any training on your data. Just download and forecast.
+  TiRex is a pre-trained model that performs time series forecasting without requiring any training on your data. Simply download and use it.
 
 - **Quantile Predictions**:
-  TiRex not only provides point estimates but provides quantile estimates.
+  TiRex provides both point estimates and quantile estimates.
 
 - **State-of-the-art Performance over Long and Short Horizons**:
   TiRex achieves top scores in various time series forecasting benchmarks, see [GiftEval](https://huggingface.co/spaces/Salesforce/GIFT-Eval) and [ChronosZS](https://huggingface.co/spaces/autogluon/fev-leaderboard).
   These benchmark show that TiRex provides great performance for both long and short-term forecasting.
 
-## Quick Start
-
-The inference code is available on [GitHub](https://github.com/NX-AI/tirex).
+## Documentation
+A detailed documentation of TiRex can be found here: https://nx-ai.github.io/tirex/
 
-### Installation
-
-TiRex is currently only tested on *Linux systems* and Nvidia GPUs with compute capability >= 8.0.
-If you want to use different systems, please check the [FAQ in the code repository](https://github.com/NX-AI/tirex?tab=readme-ov-file#faq--troubleshooting).
-It's best to install TiRex in the specified conda environment.
-The respective conda dependency file is [requirements_py26.yaml](https://github.com/NX-AI/tirex/blob/main/requirements_py26.yaml).
+## Installation
 
 ```sh
-# 1) Setup and activate conda env from ./requirements_py26.yaml
-git clone github.com/NX-AI/tirex
-conda env create --file ./tirex/requirements_py26.yaml
-conda activate tirex
-
-# 2) [Mandatory] Install Tirex
-
-## 2a) Install from source
-git clone github.com/NX-AI/tirex  # if not already cloned before
-cd tirex
-pip install -e .
+pip install tirex-ts
+```
 
-# 2b) Install from PyPi (will be available soon)
+Install with additional input/output adapter:
+```sh
+pip install "tirex-ts[gluonts,hfdataset]"
+```
 
-# 2) Optional: Install also optional dependencies
-pip install .[gluonts]      # enable gluonTS in/output API
-pip install .[hfdataset]    # enable HuggingFace datasets in/output API
-pip install .[notebooks]    # To run the example notebooks
+You can also install TiRex with all extras at once using:
+```sh
+pip install "tirex-ts[all]"
 ```
 
-### Inference Example
+TiRex is currently only tested on Linux and MacOS.
+
+## Quick Start
 
 ```python
 import torch
@@ -68,34 +74,95 @@ from tirex import load_model, ForecastModel
 
 model: ForecastModel = load_model("NX-AI/TiRex")
 data = torch.rand((5, 128))  # Sample Data (5 time series with length 128)
-forecast = model.forecast(context=data, prediction_length=64)
+quantiles, mean = model.forecast(context=data, prediction_length=64)
 ```
 
-We provide an extended quick start example in the [GitHub repository](https://github.com/NX-AI/tirex/blob/main/examples/quick_start_tirex.ipynb).
+We provide an extended quick start example in [examples/quick_start_tirex.ipynb](./examples/quick_start_tirex.ipynb).
+This notebook also shows how to use the different input and output types of you time series data.
+You can also run it in [Google Colab](https://colab.research.google.com/github/NX-AI/tirex/blob/main/examples/quick_start_tirex.ipynb).
+
+We provide notebooks to run the benchmarks: [GiftEval](./examples/gifteval/gifteval.ipynb) and [Chronos-ZS](./examples/chronos_zs/chronos_zs.ipynb).
+
+## TiRex Classification Model
+[![Paper](https://img.shields.io/static/v1?label=Paper&message=2510.26777&color=B31B1B&logo=arXiv)](http://arxiv.org/abs/2510.26777)
+
+For detailed instructions on using TiRex classification model please visit our [documentation page](https://nx-ai.github.io/tirex/how-to/classification/) and [quick start Notebook](./examples/quick_start_tirex_classification.ipynb).
+
+## TiRex Docker image
+
+For detailed instructions on building and running TiRex in a Docker container, see the [Docker README](./inference/README.md) or our [deployment documentation](https://nx-ai.github.io/tirex/deployment).
 
-### Troubleshooting / FAQ
+## Finetuning TiRex
+TiRex already provide state-of-the-art performance for zero-shot prediction. Hence, you can use it without training on your own data.
 
-If you have problems please check the FAQ / Troubleshooting section in the [GitHub repository](https://github.com/NX-AI/tirex)
-and feel free to create a GitHub issue or start a discussion.
+If you are interested in models fine-tuned on your data or with different pretraining, please contact us at [[email protected]](mailto:contact@nx-ai.com)
 
+## Troubleshooting
+If you have problems please check the [documentation](https://nx-ai.github.io/tirex/) and feel free to create a GitHub issue or start a discussion.
 
-### Training Data
 
-- [chronos_datasets](https://huggingface.co/datasets/autogluon/chronos_datasets) (Subset - Zero Shot Benchmark data is not used for training - details in the paper)
-- [GiftEvalPretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain) (Subset - details in the paper)
-- Synthetic Data
+## CUDA Kernels
+
+Tirex can use custom CUDA kernels for the sLSTM cells.
+These CUDA kernels are compiled when the model is loaded the first time.
+The CUDA kernels require GPU hardware that support CUDA compute capability 8.0 or later.
+We also highly suggest to use the provided [conda environment spec](./requirements_cu124.yaml).
+The CUDA kernels are automatically used when the xlstm package is installed.
+
+To install TiRex with the CUDA kernels run:
+```sh
+pip install "tirex-ts[cuda,gluonts,hfdataset]"
+```
+
+Explicitly set the custom CUDA backend:
+```python
+model = load_model("NX-AI/TiRex", backend="cuda")
+```
+
+### Troubleshooting CUDA
+
+**This information is taken from the
+[xLSTM repository](https://github.com/NX-AI/xlstm) - See this for further details**:
+
+For the CUDA version of sLSTM, you need to specify Compute Capability, see [https://developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus). Or just specify a range as in the example below:
+```bash
+export TORCH_CUDA_ARCH_LIST="8.0;8.6;9.0"
+```
+
+For all kinds of custom setups with torch and CUDA, keep in mind that versions have to match. Also, to make sure the correct CUDA libraries are included you can use the `XLSTM_EXTRA_INCLUDE_PATHS` environment variable now to inject different include paths, for example:
+
+```bash
+export XLSTM_EXTRA_INCLUDE_PATHS='/usr/local/include/cuda/:/usr/include/cuda/'
+```
+
+or within python:
+
+```python
+import os
+os.environ['XLSTM_EXTRA_INCLUDE_PATHS']='/usr/local/include/cuda/:/usr/include/cuda/'
+```
+
 
 ## Cite
 
-If you use TiRex in your research, please cite our work: 
+If you use TiRex in your research, please cite our work:
 
 ```bibtex
-@article{auerTiRexZeroShotForecasting2025,
+@inproceedings{auer:25tirex,
   title = {{{TiRex}}: {{Zero-Shot Forecasting Across Long}} and {{Short Horizons}} with {{Enhanced In-Context Learning}}},
-  author = {Auer, Andreas and Podest, Patrick and Klotz, Daniel and B{\"o}ck, Sebastian and Klambauer, G{\"u}nter and Hochreiter, Sepp},
-  journal = {ArXiv},
-  volume = {2505.23719},   
+  author = {Andreas Auer and Patrick Podest and Daniel Klotz and Sebastian B{\"o}ck and G{\"u}nter Klambauer and Sepp Hochreiter},
+  booktitle = {The Thirty-Ninth Annual Conference on Neural Information Processing Systems},
   year = {2025}
+  url = {https://arxiv.org/abs/2505.23719},
+}
+```
+```bibtex
+@inproceedings{auer:25tirexclassification,
+    title = {Pre-trained Forecasting Models: Strong Zero-Shot Feature Extractors for Time Series Classification},
+    author = {Andreas Auer and Daniel Klotz and Sebastinan B{\"o}ck and Sepp Hochreiter},
+    booktitle = {NeurIPS 2025 Workshop on Recent Advances in Time Series Foundation Models (BERT2S)},
+    year = {2025},
+    url = {https://arxiv.org/abs/2510.26777},
 }
 ```