Update README.md

README.md

@@ -46,11 +46,18 @@ We evaluate the benefits of pretraining DNA FM 7B by conducting a comprehensive
 <center><img src="circle_benchmarks.png" alt="Downstream results of DNA FM 7B" style="width:70%; height:auto;" /></center>
 
 ## 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_dna_7b --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset>
+mgen test --model SequenceClassification --model.backbone aido_dna_7b --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": "dnafm"}).eval()
+from modelgenerator.tasks import Embed
+model = Embed.from_config({"model.backbone": "aido_dna_7b"}).eval()
 collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
 embedding = model(collated_batch)
 print(embedding.shape)
@@ -59,8 +66,8 @@ print(embedding)
 #### Sequence Level Classification
 ```python
 import torch
-from genbio_finetune.tasks import SequenceClassification
-model = SequenceClassification.from_config({"model.backbone": "dnafm", "model.n_classes": 2}).eval()
+from modelgenerator.tasks import SequenceClassification
+model = SequenceClassification.from_config({"model.backbone": "aido_dna_7b", "model.n_classes": 2}).eval()
 collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
 logits = model(collated_batch)
 print(logits)
@@ -69,8 +76,8 @@ print(torch.argmax(logits, dim=-1))
 #### Token Level Classification
 ```python
 import torch
-from genbio_finetune.tasks import TokenClassification
-model = TokenClassification.from_config({"model.backbone": "dnafm", "model.n_classes": 3}).eval()
+from modelgenerator.tasks import TokenClassification
+model = TokenClassification.from_config({"model.backbone": "aido_dna_7b", "model.n_classes": 3}).eval()
 collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
 logits = model(collated_batch)
 print(logits)
@@ -78,18 +85,12 @@ print(torch.argmax(logits, dim=-1))
 ```
 #### Regression
 ```python
-from genbio_finetune.tasks import SequenceRegression
-model = SequenceRegression.from_config({"model.backbone": "dnafm"}).eval()
+from modelgenerator.tasks import SequenceRegression
+model = SequenceRegression.from_config({"model.backbone": "aido_dna_7b"}).eval()
 collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
 logits = model(collated_batch)
 print(logits)
 ```
-#### Or use our one-liner CLI to finetune or evaluate any of the above!
-```
-gbft fit --model SequenceClassification --model.backbone dnafm --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
-gbft test --model SequenceClassification --model.backbone dnafm --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
-```
-For more information, visit: [Model Generator](https://github.com/genbio-ai/modelgenerator)
 
 
 ## Citation
@@ -101,7 +102,4 @@ author={Caleb Ellington, Ning Sun, Nicholas Ho, Tianhua Tao, Sazan Mahbub, Yongh
 booktitle={NeurIPS 2024 Workshop on AI for New Drug Modalities},
 year={2024}
 }
-```
-
-## License
-@Hongyi TODO
+```