create MODELCARD

README.md

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+license: apache-2.0
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+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66276727368ec2a0b933772c/dPvMCbnSDjhZG_ddCw1kk.png)
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+ [![Python](https://img.shields.io/badge/Python-3.10%2B-blue)](https://www.python.org/)  [![PyTorch](https://img.shields.io/badge/PyTorch-2.4.1-blue)](https://pytorch.org/) 
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+# FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting
+
+This is the official repository of **FLAME**: Flow Enhanced Legendre Memory Models for General Time Series Forecasting.
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+
+
+## Introduction
+FLAME is a family of extremely **lightweight** and highly capable time series foundation models. Based on the normalization-based forecasting head, it can support both the **deterministic** and **probabilistic** forecasting.
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+To our best knowldege, FLAME is the first time series foundation model possessing both lightweight backbones and generative prediction capabilities!
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+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66276727368ec2a0b933772c/OHKXxjrwYR3n3LO6UeVRS.png)
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+## Architecture
+
+FLAME adopts the Channel-Independent pretraining paradigm, and each variable is preprocessed through Instance Normalization to mitigate the value discrepancy. FLAME utilizes the Re-Norm to further mitigate the statistical differences between inputs and forecasts, and its backbone mainly consists of three modules: 1) Encoding, including Time Series Tokenization, **Local-Perception**, and MSA-Encoder, which tokenize the time series and enhance them through fusing the local environmental information with LegT; 2) Decoding, including **LegS based SSD-Decoder** and MCA-Enhancer, which utilize the SSD layers and MCA layers to make long-term inference ; 3) **Flow-based Head**, which leverages the Normalization Flow to support generative probabilistic forecasting, with both efficiency and accuracy. 
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+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66276727368ec2a0b933772c/LchwMIvetMu10EjWjcW5N.png)
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+
+## Quickstart
+
+You need to install the following packages:
+
+```shell
+pip install transformers[torch]
+
+pip install mamba-ssm[causal-conv1d]
+
+pip install zuko
+```
+
+For mamba-ssm, there exists a bug about triton. We fix it and release the code at [./flame/mamba/mamba_ssm](./flame/mamba/mamba_ssm), you can compile it from scratch or simply replace the mamba_ssm package under the default path of conda.
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