Add the practice

README.md

@@ -45,27 +45,6 @@ Please try to reproduc the zero-shot experiments on ETTh2 [[here on Colab]](http
 We use the following Colab page to show the demo of building the customer dataset and directly do the inference via our pre-trained foundation model: [[Colab]](https://colab.research.google.com/drive/1ZpWbK0L6mq1pav2yDqOuORo4rHbv80-A?usp=sharing)
 
 
-## ⏳ Upcoming Features
-
-- [✅] Parallel pre-training pipeline
-- [] Probabilistic forecasting
-- [] Multimodal dataset
-- [] Multimodal pre-training script
-
-## 🚀 News
-
-
-- **Oct 2024**: 🚀 We've streamlined our code structure, enabling users to download the pre-trained model and perform zero-shot inference with a single line of code! Check out our [demo](./run_TEMPO_demo.py) for more details. Our model's download count on HuggingFace is now trackable!
-
-- **Jun 2024**: 🚀 We added demos for reproducing zero-shot experiments in [Colab](https://colab.research.google.com/drive/11qGpT7H1JMaTlMlm9WtHFZ3_cJz7p-og?usp=sharing).  We also added the demo of building the customer dataset and directly do the inference via our pre-trained foundation model: [Colab](https://colab.research.google.com/drive/1ZpWbK0L6mq1pav2yDqOuORo4rHbv80-A?usp=sharing)
-- **May 2024**: 🚀 TEMPO has launched a GUI-based online [demo](https://4171a8a7484b3e9148.gradio.live/), allowing users to directly interact with our foundation model!
-- **May 2024**: 🚀 TEMPO published the 80M pretrained foundation model in [HuggingFace](https://huggingface.co/Melady/TEMPO)!
-- **May 2024**: 🧪 We added the code for pretraining and inference TEMPO models.  You can find a pre-training script demo in [this folder](./scripts/etth2.sh). We also added [a script](./scripts/etth2_test.sh) for the inference demo.
-
-- **Mar 2024**: 📈  Released [TETS dataset](https://drive.google.com/file/d/1Hu2KFj0kp4kIIpjbss2ciLCV_KiBreoJ/view?usp=drive_link) from [S&P 500](https://www.spglobal.com/spdji/en/indices/equity/sp-500/#overview) used in multimodal experiments in TEMPO. 
-- **Mar 2024**: 🧪 TEMPO published the project [code](https://github.com/DC-research/TEMPO) and the pre-trained checkpoint [online](https://drive.google.com/file/d/11Ho_seP9NGh-lQCyBkvQhAQFy_3XVwKp/view?usp=drive_link)! 
-- **Jan 2024**: 🚀 TEMPO [paper](https://openreview.net/pdf?id=YH5w12OUuU) get accepted by ICLR!
-- **Oct 2023**: 🚀 TEMPO [paper](https://arxiv.org/pdf/2310.04948) released on Arxiv!
 
 
 # Practice
@@ -136,7 +115,8 @@ print(predicted_values)
 
 Please try our foundation model demo [[here]](https://4171a8a7484b3e9148.gradio.live).
 
-<div align="center"><img src=./pics/TEMPO_demo.jpg width=80% /></div>
+![TEMPO_demo.jpg](pics/TEMPO_demo.jpg)
+
 
 ## Practice on your end
 
@@ -191,6 +171,31 @@ Example of generated contextual information for the Company marked above:
 You can download the processed data with text embedding from GPT2 from: [[TETS]](https://drive.google.com/file/d/1Hu2KFj0kp4kIIpjbss2ciLCV_KiBreoJ/view?usp=drive_link
 ).
 
+## 🚀 News
+
+
+- **Oct 2024**: 🚀 We've streamlined our code structure, enabling users to download the pre-trained model and perform zero-shot inference with a single line of code! Check out our [demo](./run_TEMPO_demo.py) for more details. Our model's download count on HuggingFace is now trackable!
+
+- **Jun 2024**: 🚀 We added demos for reproducing zero-shot experiments in [Colab](https://colab.research.google.com/drive/11qGpT7H1JMaTlMlm9WtHFZ3_cJz7p-og?usp=sharing).  We also added the demo of building the customer dataset and directly do the inference via our pre-trained foundation model: [Colab](https://colab.research.google.com/drive/1ZpWbK0L6mq1pav2yDqOuORo4rHbv80-A?usp=sharing)
+- **May 2024**: 🚀 TEMPO has launched a GUI-based online [demo](https://4171a8a7484b3e9148.gradio.live/), allowing users to directly interact with our foundation model!
+- **May 2024**: 🚀 TEMPO published the 80M pretrained foundation model in [HuggingFace](https://huggingface.co/Melady/TEMPO)!
+- **May 2024**: 🧪 We added the code for pretraining and inference TEMPO models.  You can find a pre-training script demo in [this folder](./scripts/etth2.sh). We also added [a script](./scripts/etth2_test.sh) for the inference demo.
+
+- **Mar 2024**: 📈  Released [TETS dataset](https://drive.google.com/file/d/1Hu2KFj0kp4kIIpjbss2ciLCV_KiBreoJ/view?usp=drive_link) from [S&P 500](https://www.spglobal.com/spdji/en/indices/equity/sp-500/#overview) used in multimodal experiments in TEMPO. 
+- **Mar 2024**: 🧪 TEMPO published the project [code](https://github.com/DC-research/TEMPO) and the pre-trained checkpoint [online](https://drive.google.com/file/d/11Ho_seP9NGh-lQCyBkvQhAQFy_3XVwKp/view?usp=drive_link)! 
+- **Jan 2024**: 🚀 TEMPO [paper](https://openreview.net/pdf?id=YH5w12OUuU) get accepted by ICLR!
+- **Oct 2023**: 🚀 TEMPO [paper](https://arxiv.org/pdf/2310.04948) released on Arxiv!
+
+
+
+## ⏳ Upcoming Features
+
+- [✅] Parallel pre-training pipeline
+- [] Probabilistic forecasting
+- [] Multimodal dataset
+- [] Multimodal pre-training script
+
+
 ## Contact
 Feel free to connect DefuCao@USC.EDU / YanLiu.CS@USC.EDU if you’re interested in applying TEMPO to your real-world application.