# Build any downstream models from this backbone
## Embedding
```python
from genbio_finetune.tasks import Embed

model = Embed.from_config({"model.backbone": "dna300m"})

collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
embedding = model(collated_batch)
print(embedding.shape)
print(embedding)
```
## Sequence Level Classification
```python
import torch
from genbio_finetune.tasks import SequenceClassification

model = SequenceClassification.from_config({"model.backbone": "dna300m", "model.n_classes": 2})

collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
logits = model(collated_batch)
print(logits)
print(torch.argmax(logits, dim=-1))
```
## Token Level Classification
```python
import torch
from genbio_finetune.tasks import TokenClassification

model = TokenClassification.from_config({"model.backbone": "dna300m", "model.n_classes": 3})

collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
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": "dna300m"})

collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
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 dna300m --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
gbft test --model SequenceClassification --model.backbone dna300m --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
```
For more information, visit: [Model Generator](https://github.com/genbio-ai/test)