Update README.md

README.md

@@ -9,7 +9,6 @@ tags:
 - time-series
 ---
 
-
 # Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
 
 ![lag-llama-architecture](images/lagllama.webp)
@@ -18,19 +17,16 @@ Lag-Llama is the <b>first open-source foundation model for time series forecasti
 
 [[Tweet Thread](https://twitter.com/arjunashok37/status/1755261111233114165)] 
 
-[[Code](https://github.com/time-series-foundation-models/lag-llama)] [[Model Weights](https://huggingface.co/time-series-foundation-models/Lag-Llama)] [[Colab Demo 1: Zero-Shot Forecasting](https://colab.research.google.com/drive/1DRAzLUPxsd-0r8b-o4nlyFXrjw_ZajJJ?usp=sharing)] [[Colab Demo 2: (Preliminary Finetuning)](https://colab.research.google.com/drive/1uvTmh-pe1zO5TeaaRVDdoEWJ5dFDI-pA?usp=sharing)]
+[[Model Weights](https://huggingface.co/time-series-foundation-models/Lag-Llama)] [[Colab Demo 1: Zero-Shot Forecasting](https://colab.research.google.com/drive/1DRAzLUPxsd-0r8b-o4nlyFXrjw_ZajJJ?usp=sharing)] [[Colab Demo 2: (Preliminary Finetuning)](https://colab.research.google.com/drive/1uvTmh-pe1zO5TeaaRVDdoEWJ5dFDI-pA?usp=sharing)]
 
 [[Paper](https://arxiv.org/abs/2310.08278)]
 
 [[Video](https://www.youtube.com/watch?v=Mf2FOzDPxck)]
-
-____
-This HuggingFace model houses the <a href="https://huggingface.co/time-series-foundation-models/Lag-Llama/blob/main/lag-llama.ckpt" target="_blank">pretrained checkpoint</a> of Lag-Llama.
-
 ____
 
 <b>Updates</b>:
 
+* **16-Apr-2024**: Released pretraining and finetuning scripts to replicate the experiments in the paper. See [Reproducing Experiments in the Paper](https://github.com/time-series-foundation-models/lag-llama?tab=readme-ov-file#reproducing-experiments-in-the-paper) for details.
 * **9-Apr-2024**: We have released a 15-minute video 🎥 on Lag-Llama on [YouTube](https://www.youtube.com/watch?v=Mf2FOzDPxck).
 * **5-Apr-2024**: Added a [section](https://colab.research.google.com/drive/1DRAzLUPxsd-0r8b-o4nlyFXrjw_ZajJJ?authuser=1#scrollTo=Mj9LXMpJ01d7&line=6&uniqifier=1) in Colab Demo 1 on the importance of tuning the context length for zero-shot forecasting. Added a [best practices section](https://github.com/time-series-foundation-models/lag-llama?tab=readme-ov-file#best-practices) in the README; added recommendations for finetuning. These recommendations will be demonstrated with an example in [Colab Demo 2](https://colab.research.google.com/drive/1uvTmh-pe1zO5TeaaRVDdoEWJ5dFDI-pA?usp=sharing) soon.
 * **4-Apr-2024**: We have updated our requirements file with new versions of certain packages. Please update/recreate your environments if you have previously used the code locally.
@@ -40,30 +36,22 @@ ____
 
 ____
 
-Current Features:
+**Current Features**:
 
 💫 <b>Zero-shot forecasting</b> on a dataset of <b>any frequency</b> for <b>any prediction length</b>, using <a href="https://colab.research.google.com/drive/1DRAzLUPxsd-0r8b-o4nlyFXrjw_ZajJJ?usp=sharing" target="_blank">Colab Demo 1.</a><br/>
 
 💫 <b>Finetuning</b> on a dataset using [Colab Demo 2](https://colab.research.google.com/drive/1uvTmh-pe1zO5TeaaRVDdoEWJ5dFDI-pA?usp=sharing).
 
-**Note**: Please see the [best practices section](https://github.com/time-series-foundation-models/lag-llama?tab=readme-ov-file#best-practices) when using the model for zero-shot prediction and finetuning.
-
+💫 <b>Reproducing</b> experiments in the paper using the released scripts. See [Reproducing Experiments in the Paper](https://github.com/time-series-foundation-models/lag-llama?tab=readme-ov-file#reproducing-experiments-in-the-paper) for details. 
 
-____
-
-Coming Soon:
-
-⭐ Scripts to pretrain Lag-Llama on your own large-scale data
-
-⭐ Scripts to <b>reproduce</b> all results in the paper.
+**Note**: Please see the [best practices section](https://github.com/time-series-foundation-models/lag-llama?tab=readme-ov-file#best-practices) when using the model for zero-shot prediction and finetuning.
 
 ____
 
-We are currently looking for contributors for the following:
+## Reproducing Experiments in the Paper
 
-⭐ An <b>online gradio demo</b> where you can upload time series and get zero-shot predictions and perform finetuning.
+To replicate the pretraining setup used in the paper, please see [the pretraining script](scripts/pretrain.sh). Once a model is pretrained, instructions to finetune it with the setup in the paper can be found in [the finetuning script](scripts/finetune.sh).
 
-____
 
 ## Best Practices