[!TIP] The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation.

The team releasing RiNALMo did not write this model card for this model so this model card has been written by the MultiMolecule team.

Model Details

RiNALMo is a bert-style model pre-trained on a large corpus of non-coding RNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the Training Details section for more information on the training process.

This checkpoint is the RiNALMo-Giga model fine-tuned on bpRNA-1m for RNA secondary structure prediction.

Variants

multimolecule/rinalmo-giga: The RiNALMo model with 650 million parameters.
multimolecule/rinalmo-mega: The RiNALMo model with 150 million parameters.
multimolecule/rinalmo-micro: The RiNALMo model with 30 million parameters.

Model Specification

Variants	Num Layers	Hidden Size	Num Heads	Intermediate Size	Num Parameters (M)	FLOPs (G)	MACs (G)	Max Num Tokens
RiNALMo-Giga	33	1280	20	5120	650.88	709.59	354.31	1022
RiNALMo-Mega	30	640	2560	148.04	171.76	85.54
RiNALMo-Micro	12	480	1920	33.48	40.26	20.01

Usage

The model file depends on the multimolecule library. You can install it using pip:

pip install multimolecule

Direct Use

RNA Secondary Structure Prediction

You can use this model directly with a pipeline for secondary structure prediction:

python

import multimolecule  # you must import multimolecule to register models
from transformers import pipeline

predictor = pipeline("rna-secondary-structure", model="multimolecule/rinalmo-giga-ss")
output = predictor("GGUCUCUCUGGUUAGACCAGAUCUGAGCCU")

Downstream Use

Extract Features

Here is how to use this model to get the features of a given sequence in PyTorch:

python

from multimolecule import RnaTokenizer, RiNALMoModel


tokenizer = RnaTokenizer.from_pretrained("multimolecule/rinalmo-giga-ss")
model = RiNALMoModel.from_pretrained("multimolecule/rinalmo-giga-ss")

text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")

output = model(**input)

Sequence Classification / Regression

[!NOTE] This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression.

Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch:

python

import torch
from multimolecule import RnaTokenizer, RiNALMoForSequencePrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/rinalmo-giga-ss")
model = RiNALMoForSequencePrediction.from_pretrained("multimolecule/rinalmo-giga-ss")

text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.tensor([1])

output = model(**input, labels=label)

Token Classification / Regression

[!NOTE] This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for token classification or regression.

Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch:

python

import torch
from multimolecule import RnaTokenizer, RiNALMoForTokenPrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/rinalmo-giga-ss")
model = RiNALMoForTokenPrediction.from_pretrained("multimolecule/rinalmo-giga-ss")

text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), ))

output = model(**input, labels=label)

Contact Classification / Regression

[!NOTE] This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression.

Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch:

python

import torch
from multimolecule import RnaTokenizer, RiNALMoForContactPrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/rinalmo-giga-ss")
model = RiNALMoForContactPrediction.from_pretrained("multimolecule/rinalmo-giga-ss")

text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), len(text)))

output = model(**input, labels=label)

Training Details

RiNALMo used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling.

Training Data

The RiNALMo model was pre-trained on a cocktail of databases including RNAcentral, Rfam, Ensembl Genome Browser, and Nucleotide. The training data contains 36 million unique ncRNA sequences.

To ensure sequence diversity in each training batch, RiNALMo clustered the sequences with MMSeqs2 into 17 million clusters and then sampled each sequence in the batch from a different cluster.

RiNALMo preprocessed all tokens by replacing "U"s with "T"s.

Note that during model conversions, "T" is replaced with "U". [RnaTokenizer][multimolecule.RnaTokenizer] will convert "T"s to "U"s for you, you may disable this behaviour by passing replace_T_with_U=False.

Training Procedure

Preprocessing

RiNALMo used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT:

Mask rate: 15%
Replacement: <mask> for 80% of masked tokens
Replacement: random token for 10% of masked tokens
Replacement: unchanged token for 10% of masked tokens

Pre-training

The model was trained on 7 NVIDIA A100 GPUs with 80GiB memories.

Batch Size: 1344
Epochs: 6
Learning rate: 5e-5
Learning rate scheduler: Cosine
Learning rate warm-up: 2,000 steps
Learning rate minimum: 1e-5
Dropout: 0.1

Fine-tuning

This checkpoint was fine-tuned for RNA secondary structure prediction on the bpRNA-1m dataset.

Citation

bibtex

@ARTICLE{Penic2025-qf,
  title     = "{RiNALMo}: general-purpose {RNA} language models can generalize
               well on structure prediction tasks",
  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",
  abstract  = "While RNA has recently been recognized as an interesting
               small-molecule drug target, many challenges remain to be
               addressed before we take full advantage of it. This emphasizes
               the necessity to improve our understanding of its structures and
               functions. Over the years, sequencing technologies have produced
               an enormous amount of unlabeled RNA data, which hides a huge
               potential. Motivated by the successes of protein language
               models, we introduce RiboNucleic Acid Language Model (RiNALMo)
               to unveil the hidden code of RNA. RiNALMo is the largest RNA
               language model to date, with 650M parameters pre-trained on 36M
               non-coding RNA sequences from several databases. It can extract
               hidden knowledge and capture the underlying structure
               information implicitly embedded within the RNA sequences.
               RiNALMo achieves state-of-the-art results on several downstream
               tasks. Notably, we show that its generalization capabilities
               overcome the inability of other deep learning methods for
               secondary structure prediction to generalize on unseen RNA
               families.",
  journal   = "Nature Communications",
  publisher = "Springer Science and Business Media LLC",
  volume    =  16,
  number    =  1,
  pages     = "5671",
  month     =  jul,
  year      =  2025,
  copyright = "https://creativecommons.org/licenses/by-nc-nd/4.0",
  language  = "en"
}

[!NOTE] The artifacts distributed in this repository are part of the MultiMolecule project. If MultiMolecule supports your research, please cite the MultiMolecule project as follows:

bibtex

@software{chen_2024_12638419,
  author    = {Chen, Zhiyuan and Zhu, Sophia Y.},
  title     = {MultiMolecule},
  doi       = {10.5281/zenodo.12638419},
  publisher = {Zenodo},
  url       = {https://doi.org/10.5281/zenodo.12638419},
  year      = 2024,
  month     = may,
  day       = 4
}

Contact

Please use GitHub issues of MultiMolecule for any questions or comments on the model card.

Please contact the authors of the RiNALMo paper for questions or comments on the paper/model.

License

This model implementation is licensed under the GNU Affero General Public License.

For additional terms and clarifications, please refer to our License FAQ.

SPDX-License-Identifier: AGPL-3.0-or-later

multimolecule/rinalmo-giga-ss

RiNALMo

Disclaimer

Model Details

Variants

Model Specification

Links

Usage

Direct Use

RNA Secondary Structure Prediction

Downstream Use

Extract Features

Sequence Classification / Regression

Token Classification / Regression

Contact Classification / Regression

Training Details

Training Data

Training Procedure

Preprocessing

Pre-training

Fine-tuning

Citation

Contact

License

multimolecule/
rinalmo-giga-ss