---
license: other
---
# AIDO.RNA-1.6B

AIDO.RNA-1.6B is a general-purpose RNA foundation model with 1.6 billion parameters, trained on 42 million non-coding RNA sequences at single-nucleotide resolution. It achieves state-of-the-art performance on a comprehensive set of tasks, including RNA secondary structure prediction, mRNA-related tasks, RNA function prediction, and RNA inverse folding. After domain adaptation, AIDO.RNA excels in modeling protein-level tasks, highlighting its potential to leverage the central dogma for enhancing biomolecular representations. For more detailed information, please refer to [our paper](https://www.biorxiv.org/content/10.1101/2024.11.28.625345v1).

<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/63008d4bc1e149ceaff724a3/mNqn5SKQFHxSby3E2dosE.png" alt="description" style="width:80%; height:auto;">
</p>

## Model architectural details
AIDO.RNA is an encoder-only transformer and is pre-trained using masked language modeling (MLM) objective. The model architecture parameters are as follows:
|   hyperparameter  |  value     |
| :---:             |    :----:  |
| num-layers        | 32         |
| hidden-size       | 2,048      |
| ffn-hidden-size   | 5,440      |
| num-attn-heads    | 32         |
| vocab-size        | 16         |


## Pre-training data
The pre-training data contains 42 million unique ncRNA sequences from RNAcentral version 24.0. 
<p align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/63008d4bc1e149ceaff724a3/EKvuUI9mBw5hkErzpXKm9.png" alt="description" style="width:90%; height:auto;">
</p>

## Downstream evaluation
<p align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/63008d4bc1e149ceaff724a3/uvII1Q_1vDe95WCP1RgUV.png" alt="description" style="width:90%; height:auto;">
</p>


## How to Use
### 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_rna_1b600m --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset>
mgen test --model SequenceClassification --model.backbone aido_rna_1b600m --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset>
```

### Or use directly in Python
#### Embedding
```python
from modelgenerator.tasks import Embed
model = Embed.from_config({"model.backbone": "aido_rna_1b600m"}).eval()
transformed_batch = model.transform({"sequences": ["ACGT", "AGCT"]})
embedding = model(transformed_batch)
print(embedding.shape)
print(embedding)
```
#### Sequence-level Classification
```python
import torch
from modelgenerator.tasks import SequenceClassification
model = SequenceClassification.from_config({"model.backbone": "aido_rna_1b600m", "model.n_classes": 2}).eval()
transformed_batch = model.transform({"sequences": ["ACGT", "AGCT"]})
logits = model(transformed_batch)
print(logits)
print(torch.argmax(logits, dim=-1))
```
#### Token-level Classification
```python
import torch
from modelgenerator.tasks import TokenClassification
model = TokenClassification.from_config({"model.backbone": "aido_rna_1b600m", "model.n_classes": 3}).eval()
transformed_batch = model.transform({"sequences": ["ACGT", "AGCT"]})
logits = model(transformed_batch)
print(logits)
print(torch.argmax(logits, dim=-1))
```
#### Sequence-level Regression
```python
from modelgenerator.tasks import SequenceRegression
model = SequenceRegression.from_config({"model.backbone": "aido_rna_1b600m"}).eval()
transformed_batch = model.transform({"sequences": ["ACGT", "AGCT"]})
logits = model(transformed_batch)
print(logits)
```

### Get RNA sequence embedding
```python
from genbio_finetune.tasks import Embed
model = Embed.from_config({"model.backbone": "aido_rna_1b600m"}).eval()
transformed_batch = model.transform({"sequences": ["ACGT", "ACGT"]})
embedding = model(transformed_batch)
print(embedding.shape)
print(embedding)
```

## Citation
Please cite AIDO.RNA using the following BibTeX code:
```
@inproceedings{zou_large-scale_2024,
	title = {A Large-Scale Foundation Model for RNA Function and Structure Prediction},
	url = {https://www.biorxiv.org/content/10.1101/2024.11.28.625345v1},
	doi = {10.1101/2024.11.28.625345},
	publisher = {bioRxiv},
	author = {Zou, Shuxian and Tao, Tianhua and Mahbub, Sazan and Ellington, Caleb N. and Algayres, Robin and Li, Dian and Zhuang, Yonghao and Wang, Hongyi and Song, Le and Xing, Eric P.},
	year = {2024},
    booktitle = {NeurIPS 2024 Workshop on AI for New Drug Modalities},
}
```