RNA-FM

A 12-layer BERT-style transformer pre-trained on 23.7 million non-coding RNA sequences via masked language modelling.

Architecture

Parameter	Value
Layers	12
Attention heads	20
Embedding dimension	640
FFN dimension	5120
Vocabulary size	25
Positional encoding	Learned
Architecture	ESM-1b-style pre-LN Transformer
Max sequence length	1024 tokens

Vocabulary: <cls>, <pad>, <eos>, <unk>, A, C, G, U, R, Y, K, M, S, W, B, D, H, V, N, -, and 4 null-padding tokens, <mask>.

Pretraining

• Objective: Masked language modelling (BERT-style, 15% masking rate)
• Data: RNAcentral100 -- 23.7 million non-coding RNA sequences
• Source checkpoint: RNA-FM_pretrained.pth from cuhkaih/rnafm

Parity Verification

Hidden-state representations verified identical (max abs diff = 0.00) to the original implementation at all 13 representation levels (embedding + 12 transformer layers). Verified on GPU (CUDA) with PyTorch 2.7 / transformers 4.57.6. SDPA numerical differences are expected (~1e-4 max diff over 12 layers) and are not a correctness issue.

Related Models

See the full RNA-FM collection.

Model	Training data	Embedding dim	Notes
RNA-FM	23.7 M ncRNA	640	This model
mRNA-FM	45 M CDS	1280	Codon (3-mer) tokenisation

Usage

Embedding generation

import torch
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("Taykhoom/RNA-FM", trust_remote_code=True)
model = AutoModel.from_pretrained("Taykhoom/RNA-FM", trust_remote_code=True)
model.eval()

sequences = [
    "GGGUGCGAUCAUACCAGCACUAAUGCCCUCCUGGGAAGUCCUCGUGUUGCACCCCU",
    "AUCGGGCUUAGCAUAGCUU",
]
# RNA-FM was trained on RNA sequences (U not T). T is not in the vocabulary.
# If your sequences use DNA notation, convert first:
#   sequences = [s.replace("T", "U") for s in sequences]
enc = tokenizer(sequences, return_tensors="pt", padding=True)

with torch.no_grad():
    out = model(**enc)

cls_emb   = out.last_hidden_state[:, 0, :]   # (batch, 640) -- CLS token
token_emb = out.last_hidden_state             # (batch, seq_len, 640) -- per-token

# Intermediate layers
out_all = model(**enc, output_hidden_states=True)
layer6_emb = out_all.hidden_states[6]         # layer 0 = embedding, 1-12 = transformer layers

MLM logits

import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("Taykhoom/RNA-FM", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Taykhoom/RNA-FM", trust_remote_code=True)
model.eval()

enc = tokenizer(["GGG<mask>GCGAU"], return_tensors="pt")
with torch.no_grad():
    logits = model(**enc).logits   # (1, seq_len, 25)

Fine-tuning

Standard HF conventions. Use the CLS token embedding (out.last_hidden_state[:, 0, :]) as input to a classification or regression head for sequence-level tasks.

Implementation Notes

The original implementation uses F.multi_head_attention_forward (eager). This HF port adds attn_implementation="sdpa" and attn_implementation="flash_attention_2" support, which were not part of the original codebase.

Input sequences are expected to use RNA notation (U not T).

Citation

@article{chen2022_rnafm,
  title   = {Interpretable {RNA} Foundation Model from Unannotated Data for Highly Accurate {RNA} Structure and Function Predictions},
  author  = {Chen, Jiayang and Hu, Zhihang and Sun, Siqi and Tan, Qingxiong and Wang, Yixuan and Yu, Qinze and Zong, Licheng and Hong, Liang and Xiao, Jin and Shen, Tao and King, Irwin and Li, Yu},
  journal = {arXiv preprint arXiv:2204.00300},
  year    = {2022},
  doi     = {10.48550/arXiv.2204.00300}
}

Credits

Original model and code by Chen et al. Source: GitHub. The HF conversion code was authored primarily by Claude Code and reviewed manually by Taykhoom Dalal.

License

MIT, following the original repository.