Update ReadMe

.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
+*.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,5 +1,59 @@
 ---
+license: apache-2.0
 library_name: terratorch
 ---
 
-Model weights for the TerraMind 1.0 Tokenizer for S2L2A. 
+# TerraMind 1.0 S-2 L2A Tokenizer
+
+TerraMind is the first multimodal any-to-any generative foundation model for Earth Observation jointly developed by IBM, ESA, and Forschungszentrum Jülich.
+
+The model is pre-trained using FSQ-VAE tokens as targets. This tokenizer encodes and decodes Sentinel-2 L2A satellite images.
+
+![s2l2a_tokenizer.png](assets%2Fs2l2a_tokenizer.png)
+
+The tokenizer uses FSQ with five dimensions and a codebook size of 15'360 tokens.
+It was pre-trained for 100 epochs on the S-2 L2A images from the TerraMesh dataset.
+
+## Usage
+
+The tokenizer is fully integrated into the fine-tuning toolkit [TerraTorch](https://ibm.github.io/terratorch/).
+
+You can initialize the pre-trained tokenizer with:
+```python
+from terratorch.registry import FULL_MODEL_REGISTRY
+model = FULL_MODEL_REGISTRY.build('terramind_v1_tokenizer_s2l2a', pretrained=True)
+```
+
+Once the model is build, you can run encode and decode tokens, or test the reconstruction. 
+Define the number of diffusion steps with `timesteps`.
+
+```python 
+# Encode image
+_, _, tokens = model.encode(s2l2a_tensor)
+# Decode tokens
+reconstruction = model.decode_tokens(tokens, verbose=True, timesteps=10)
+# Encode & decode
+reconstruction = model(s2l2a_tensor)
+```
+
+This tokenizer is automatically loaded with TerraMind generation models like `terramind_v1_base_generate`, see [here](https://huggingface.co/ibm-esa-geospatial/TerraMind-1.0-base#generations) for details.
+
+We provide example code for the tokenizer at https://github.com/IBM/terramind.
+
+
+## Feedback
+
+If you have feedback or any questions, please start a discussion in this HF repository or submitting an issue to [TerraMind](https://github.com/IBM/terramind) on GitHub.
+
+## Citation
+
+If you use TerraMind in your research, please cite our [TerraMind](https://arxiv.org/abs/2504.11171) pre-print.
+
+```text
+@article{jakubik2025terramind,
+  title={TerraMind: Large-Scale Generative Multimodality for Earth Observation},
+  author={Jakubik, Johannes and Yang, Felix and Blumenstiel, Benedikt and Scheurer, Erik and Sedona, Rocco and Maurogiovanni, Stefano and Bosmans, Jente and Dionelis, Nikolaos and Marsocci, Valerio and Kopp, Niklas and others},
+  journal={arXiv preprint arXiv:2504.11171},
+  year={2025}
+}
+```