* upload encoder checkpoints

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text

.ipynb_checkpoints/README-checkpoint.md

@@ -0,0 +1,29 @@
+---
+license: apache-2.0
+---
+
+This repo provides the model weights released in the paper [Towards Neural Scaling Laws for Time Series Foundation Models](https://arxiv.org/abs/2410.12360). 
+
+The models have varying sizes, ranging from 1M to 1B parameters, and were trained on datasets spanning from 10M to 16B time points.
+
+Code: https://github.com/Qingrenn/TSFM-ScalingLaws
+
+Dataset: https://huggingface.co/datasets/Qingren/TSFMScalingLaws 
+
+<p align="center">
+  <img src="figures/tsfm-scalinglaws.jpg" width="100%">
+  <br />
+  <span>
+    Figure1: Scaling laws for NLL in relation to model size, compute, and dataset size. The blue lines represent ID performance, while the red and green lines show OOD performance on LSF subset and Monash subset.
+  </span>
+</p>
+
+<p align="center">
+  <img src="figures/subjective_results.jpg" width="100%">
+  <br />
+  <span>
+    Figure2: Prediction results of models with sizes 1B, 300M, 100M, and 10M.
+  </span>
+</p>
+
+

README.md

@@ -1,3 +1,29 @@
 ---
 license: apache-2.0
 ---
+
+This repo provides the model weights released in the paper [Towards Neural Scaling Laws for Time Series Foundation Models](https://arxiv.org/abs/2410.12360). 
+
+The models have varying sizes, ranging from 1M to 1B parameters, and were trained on datasets spanning from 10M to 16B time points.
+
+Code: https://github.com/Qingrenn/TSFM-ScalingLaws
+
+Dataset: https://huggingface.co/datasets/Qingren/TSFMScalingLaws 
+
+<p align="center">
+  <img src="figures/tsfm-scalinglaws.jpg" width="100%">
+  <br />
+  <span>
+    Figure1: Scaling laws for NLL in relation to model size, compute, and dataset size. The blue lines represent ID performance, while the red and green lines show OOD performance on LSF subset and Monash subset.
+  </span>
+</p>
+
+<p align="center">
+  <img src="figures/subjective_results.jpg" width="100%">
+  <br />
+  <span>
+    Figure2: Prediction results of models with sizes 1B, 300M, 100M, and 10M.
+  </span>
+</p>
+
+

encoder_100M_16B.ckpt

@@ -0,0 +1,3 @@
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+size 511513426

encoder_10M_16B.ckpt

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+size 57547814

encoder_1B_16B.ckpt

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+size 4080900576

encoder_300M_16B.ckpt

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+size 1212117408

encoder_30M_16B.ckpt

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+size 152441950