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README.md

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 - time series foundation models
 - time-series
 ---
+
+# Chronos-T5 Mini
+
+Chronos models are pre-trained **time series forecasting models** based on language model architectures.
+A time series is transformed into a sequence of tokens via scaling and quantization, and forecasts are obtained by sampling multiple sequences of future observations given historical context.
+Chronos models are trained on a large corpus of publicly available time series data, as well as synthetic data.
+
+For details on Chronos models, training data and procedures, and experimental results, refer to the paper [Chronos: Learning the Language of Time Series](https://www.example.com/).
+
+## Architecture
+
+The model in this repository is based on the [T5 architecture](https://arxiv.org/abs/1910.10683). The only difference is in the vocabulary size: Chronos-T5 uses 4096 different tokens, compared to 32128 of the original T5 models, resulting in a smaller number of total parameters.
+
+ Model          | Parameters        | Based on
+----------------|-------------------|----------------------
+[chronos-t5-mini](https://huggingface.co/amazon/chronos-t5-mini) | 20M | [t5-efficient-mini](https://huggingface.co/google/t5-efficient-mini)
+[chronos-t5-small](https://huggingface.co/amazon/chronos-t5-small) | 46M | [flan-t5-small](https://huggingface.co/google/flan-t5-small)
+[chronos-t5-base](https://huggingface.co/amazon/chronos-t5-base) | 200M | [flan-t5-base](https://huggingface.co/google/flan-t5-base)
+[chronos-t5-large](https://huggingface.co/amazon/chronos-t5-large) | 710M | [flan-t5-large](https://huggingface.co/google/flan-t5-large)
+
+## Usage
+
+To do inference with Chronos models, refer to the code and examples in the [companion GitHub repo](https://www.example.com/).
+
+## References
+
+If you find Chronos models useful for your research, please consider citing the associated [paper](https://www.example.com/):
+
+```
+paper citation
+```