Update README.md

README.md

@@ -1,14 +1,22 @@
 # AIDO.Protein-16B-v1
-## Model Description
-We pretrained our model in three stages. This model represents the final stage, where we continued training our AIDO.Protein-16B using an additional 100 billion amino acids from Uniref90.
+
+AIDO.Protein-16B-v1 continues the pre-training of [AIDO.Protein-16B](https://huggingface.co/genbio-ai/AIDO.Protein-16B) using an additional 100 billion amino acids from Uniref90.
+
 
 ## How to Use
-### Build any downstream models from this backbone
+### Build any downstream models from this backbone with ModelGenerator
+For more information, visit: [Model Generator](https://github.com/genbio-ai/modelgenerator)
+```bash
+mgen fit --model SequenceClassification --model.backbone aido_protein_16b_v1 --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset>
+mgen test --model SequenceClassification --model.backbone aido_protein_16b_v1 --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset>
+```
+
+### Or use directly in Python
 #### Embedding
 ```python
-from genbio_finetune.tasks import Embed
-model = Embed.from_config({"model.backbone": "proteinfm_v1"}).eval()
-collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
+from modelgenerator.tasks import Embed
+model = Embed.from_config({"model.backbone": "aido_protein_16b_v1"}).eval()
+collated_batch = model.collate({"sequences": ["HELLQ", "WRLD"]})
 embedding = model(collated_batch)
 print(embedding.shape)
 print(embedding)
@@ -16,9 +24,9 @@ print(embedding)
 #### Sequence Level Classification
 ```python
 import torch
-from genbio_finetune.tasks import SequenceClassification
-model = SequenceClassification.from_config({"model.backbone": "proteinfm_v1", "model.n_classes": 2}).eval()
-collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
+from modelgenerator.tasks import SequenceClassification
+model = SequenceClassification.from_config({"model.backbone": "aido_protein_16b_v1", "model.n_classes": 2}).eval()
+collated_batch = model.collate({"sequences": ["HELLQ", "WRLD"]})
 logits = model(collated_batch)
 print(logits)
 print(torch.argmax(logits, dim=-1))
@@ -26,26 +34,32 @@ print(torch.argmax(logits, dim=-1))
 #### Token Level Classification
 ```python
 import torch
-from genbio_finetune.tasks import TokenClassification
-model = TokenClassification.from_config({"model.backbone": "proteinfm_v1", "model.n_classes": 3}).eval()
-collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
+from modelgenerator.tasks import TokenClassification
+model = TokenClassification.from_config({"model.backbone": "aido_protein_16b_v1", "model.n_classes": 3}).eval()
+collated_batch = model.collate({"sequences": ["HELLQ", "WRLD"]})
 logits = model(collated_batch)
 print(logits)
 print(torch.argmax(logits, dim=-1))
 ```
 #### Regression
 ```python
-from genbio_finetune.tasks import SequenceRegression
-model = SequenceRegression.from_config({"model.backbone": "proteinfm_v1"}).eval()
-collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
+from modelgenerator.tasks import SequenceRegression
+model = SequenceRegression.from_config({"model.backbone": "aido_protein_16b_v1"}).eval()
+collated_batch = model.collate({"sequences": ["HELLQ", "WRLD"]})
 logits = model(collated_batch)
 print(logits)
 ```
-#### Protein-Protein Interaction
 
-#### Or use our one-liner CLI to finetune or evaluate any of the above!
+# Citation
+Please cite AIDO.Protein using the following BibTex code:
 ```
-gbft fit --model SequenceClassification --model.backbone proteinfm_v1 --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
-gbft test --model SequenceClassification --model.backbone proteinfm_v1 --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
+@inproceedings{sun_mixture_2024,
+	title = {Mixture of Experts Enable Efficient and Effective Protein Understanding and Design},
+	url = {https://www.biorxiv.org/content/10.1101/2024.11.29.625425v1},
+	doi = {10.1101/2024.11.29.625425},
+	publisher = {bioRxiv},
+	author = {Sun, Ning and Zou, Shuxian and Tao, Tianhua and Mahbub, Sazan and Li, Dian and Zhuang, Yonghao and Wang, Hongyi and Cheng, Xingyi and Song, Le and Xing, Eric P.},
+	year = {2024},
+    booktitle={NeurIPS 2024 Workshop on AI for New Drug Modalities},
+}
 ```
-For more information, visit: [Model Generator](https://github.com/genbio-ai/modelgenerator)