Duplicate from autogluon/chronos-2-synth

Co-authored-by: Abdul Fatir Ansari <abdulfatir@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+model_id: chronos-2-synth
+tags:
+  - time series
+  - forecasting
+  - foundation models
+  - pretrained models
+  - safetensors
+paper:
+  - https://arxiv.org/abs/2510.15821
+pipeline_tag: time-series-forecasting
+library_name: chronos-forecasting
+
+---
+
+# Chronos-2-Synth
+
+This is a variant of the Chronos-2 model which has _only_ been trained on synthetic univariate and multivariate data. For usage and details on the Chronos-2 model, please refer to https://huggingface.co/autogluon/chronos-2.
+
+## Citation
+
+If you find Chronos-2 useful for your research, please consider citing the associated paper:
+
+```
+@article{ansari2025chronos2,
+  title        = {Chronos-2: From Univariate to Universal Forecasting},
+  author       = {Abdul Fatir Ansari and Oleksandr Shchur and Jaris Küken and Andreas Auer and Boran Han and Pedro Mercado and Syama Sundar Rangapuram and Huibin Shen and Lorenzo Stella and Xiyuan Zhang and Mononito Goswami and Shubham Kapoor and Danielle C. Maddix and Pablo Guerron and Tony Hu and Junming Yin and Nick Erickson and Prateek Mutalik Desai and Hao Wang and Huzefa Rangwala and George Karypis and Yuyang Wang and Michael Bohlke-Schneider},
+  year         = {2025},
+  url          = {https://arxiv.org/abs/2510.15821}
+}
+```

config.json

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+{
+  "architectures": [
+    "Chronos2Model"
+  ],
+  "chronos_config": {
+    "context_length": 8192,
+    "input_patch_size": 16,
+    "input_patch_stride": 16,
+    "max_output_patches": 64,
+    "output_patch_size": 16,
+    "quantiles": [
+      0.01,
+      0.05,
+      0.1,
+      0.2,
+      0.3,
+      0.4,
+      0.5,
+      0.6,
+      0.7,
+      0.8,
+      0.9,
+      0.95,
+      0.99
+    ],
+    "time_encoding_scale": 8192,
+    "use_arcsinh": true,
+    "use_reg_token": true
+  },
+  "chronos_pipeline_class": "Chronos2Pipeline",
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "dense_act_fn": "relu",
+  "dropout_rate": 0.1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 0.05,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_heads": 12,
+  "num_layers": 12,
+  "pad_token_id": 0,
+  "reg_token_id": 1,
+  "rope_theta": 10000.0,
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "use_cache": true,
+  "vocab_size": 2
+}

model.safetensors

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