Instructions to use Taykhoom/RiNALMo-micro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taykhoom/RiNALMo-micro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Taykhoom/RiNALMo-micro", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Taykhoom/RiNALMo-micro", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
CHANGED
|
@@ -136,11 +136,14 @@ even at inference, consistent with the original training code.
|
|
| 136 |
## Citation
|
| 137 |
|
| 138 |
```bibtex
|
| 139 |
-
@article{
|
| 140 |
-
title={RiNALMo:
|
| 141 |
-
author={
|
| 142 |
-
journal={
|
| 143 |
-
|
|
|
|
|
|
|
|
|
|
| 144 |
}
|
| 145 |
```
|
| 146 |
|
|
|
|
| 136 |
## Citation
|
| 137 |
|
| 138 |
```bibtex
|
| 139 |
+
@article{penic2025_rinalmo,
|
| 140 |
+
title={RiNALMo: general-purpose RNA language models can generalize well on structure prediction tasks},
|
| 141 |
+
author={Peni\'{c}, Rafael Josip and Vla\v{s}i\'{c}, Tin and Huber, Roland G. and Wan, Yue and \v{S}iki\'{c}, Mile},
|
| 142 |
+
journal={Nature Communications},
|
| 143 |
+
volume={16},
|
| 144 |
+
pages={5671},
|
| 145 |
+
year={2025},
|
| 146 |
+
doi={10.1038/s41467-025-60872-5}
|
| 147 |
}
|
| 148 |
```
|
| 149 |
|