Add model, update README

README.md

@@ -1,3 +1,57 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+pipeline_tag: time-series-forecasting
+tags:
+  - time series
+  - forecasting
+  - pretrained models
+  - foundation models
+  - time series foundation models
+  - time-series
+---
+
+# Chronos-Bolt
+
+Pre-release of Chronos-Bolt pretrained time series forecasting models. 
+
+## Usage
+
+A minimal example showing how to perform inference using Chronos-Bolt 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-base"},
+        ]
+    },
+).predict(
+    df
+)
+```
+
+## Citation
+
+If you find 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-base",
+  "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": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "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": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "pad_token_id": 0,
+  "reg_token_id": 1,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.1",
+  "use_cache": true,
+  "vocab_size": 2
+}

model.safetensors

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+oid sha256:31f875483a3215bc6880a0837ea608a13ce55f88ad90538c3cdd0b29aeb60b36
+size 821203576