Update README.md

README.md

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 # CoinRun Dataset
 
 This is a large dataset of **50M video frames** and actions collected from the **CoinRun** environment (Cobbe et al., 2020) for training world models.   
-The dataset enables reproducible, large-scale experiments in action-conditioned video prediction using repos like [Jasmine](https://github.com/p-doom/jasmine).
+The dataset enables reproducible, large-scale experiments in action-conditioned video prediction. It is meant to be used with [Jasmine](https://github.com/p-doom/jasmine), our JAX-based world modeling codebase.
 
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@@ -20,10 +20,23 @@ The dataset enables reproducible, large-scale experiments in action-conditioned
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 ## Usage
-This dataset it part of the [Jasmine](https://github.com/p-doom/jasmine) repository release. Frames were collected from random agent rollouts. 
+This dataset is part of the [Jasmine](https://github.com/p-doom/jasmine) repository release. Frames were collected from random agent rollouts. 
 
-The ArrayRecord format enables efficient dataloading using Grain ([Jasmine](https://github.com/p-doom/jasmine/blob/main/jasmine/utils/dataloader.py)).
+The ArrayRecord format enables efficient dataloading using Grain and is optimized for the [Jasmine dataloader](https://github.com/p-doom/jasmine/blob/main/jasmine/utils/dataloader.py).
 
+You can download the dataset using the `huggingface-cli` tool.
+
+```bash
+huggingface-cli download --repo-type dataset p-doom/coinrun-dataset --local-dir <data_path>
+```
+
+To start a training run using Jasmine, simply pass the `train` and `val` split to the training script. 
+```bash
+python jasmine/baselines/maskgit/train_tokenizer_vqvae.py \
+    --data_dir <data_path>/train \
+    --val_data_dir <data_path>/val \
+    ...
+```
 ## Citation
 
 If you use our CoinRun dataset, please cite our work: