Add model, update readme

README.md

@@ -1,3 +1,70 @@
 ---
 license: apache-2.0
+pipeline_tag: time-series-forecasting
+tags:
+  - time series
+  - forecasting
+  - pretrained models
+  - foundation models
+  - time series foundation models
+  - time-series
 ---
+
+# Chronos⚡️-Tiny
+
+Pre-release of Chronos⚡️ (read: Chronos-Bolt) pretrained time series forecasting models. Chronos⚡️ models are based on the [T5 architecture](https://arxiv.org/abs/1910.10683) and are available in the following sizes.
+
+
+<div align="center">
+
+| Model                                                                  | Parameters | Based on                                                               |
+| ---------------------------------------------------------------------- | ---------- | ---------------------------------------------------------------------- |
+| [**chronos-bolt-tiny**](https://huggingface.co/autogluon/chronos-bolt-tiny)   | 9M         | [t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny)   |
+| [**chronos-bolt-mini**](https://huggingface.co/autogluon/chronos-bolt-mini)   | 21M        | [t5-efficient-mini](https://huggingface.co/google/t5-efficient-mini)   |
+| [**chronos-bolt-small**](https://huggingface.co/autogluon/chronos-bolt-small) | 48M        | [t5-efficient-small](https://huggingface.co/google/t5-efficient-small) |
+| [**chronos-bolt-base**](https://huggingface.co/autogluon/chronos-bolt-base)   | 205M       | [t5-efficient-base](https://huggingface.co/google/t5-efficient-base)   |
+
+</div>
+
+
+## Usage
+
+A minimal example showing how to perform inference using Chronos⚡️ with AutoGluon:
+
+```
+pip install --pre autogluon
+```
+
+```python
+from autogluon.timeseries import TimeSeriesPredictor, TimeSeriesDataFrame
+
+df = TimeSeriesDataFrame("https://autogluon.s3.amazonaws.com/datasets/timeseries/m4_hourly/train.csv")
+
+predictions = TimeSeriesPredictor().fit(
+    df,
+    hyperparameters={
+        "Chronos": [
+            {"model_path": "autogluon/chronos-bolt-tiny"},
+        ]
+    },
+).predict(
+    df
+)
+```
+
+## Citation
+
+If you find Chronos or Chronos⚡️ models useful for your research, please consider citing the associated [paper](https://arxiv.org/abs/2403.07815):
+
+```
+@article{ansari2024chronos,
+  author  = {Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan, and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang},
+  title   = {Chronos: Learning the Language of Time Series},
+  journal = {arXiv preprint arXiv:2403.07815},
+  year    = {2024}
+}
+```
+
+## License
+
+This project is licensed under the Apache-2.0 License.

config.json

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+{
+  "_name_or_path": "autogluon/chronos-bolt-tiny",
+  "architectures": [
+    "ChronosBoltModelForForecasting"
+  ],
+  "chronos_config": {
+    "context_length": 2048,
+    "input_patch_size": 16,
+    "input_patch_stride": 16,
+    "prediction_length": 64,
+    "quantiles": [
+      0.1,
+      0.2,
+      0.3,
+      0.4,
+      0.5,
+      0.6,
+      0.7,
+      0.8,
+      0.9
+    ],
+    "use_reg_token": true
+  },
+  "chronos_pipeline_class": "ChronosBoltPipeline",
+  "classifier_dropout": 0.0,
+  "d_ff": 1024,
+  "d_kv": 64,
+  "d_model": 256,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "relu",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 0.05,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 4,
+  "num_heads": 4,
+  "num_layers": 4,
+  "pad_token_id": 0,
+  "reg_token_id": 1,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.3",
+  "use_cache": true,
+  "vocab_size": 2
+}

model.safetensors

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