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 hidden dimension | 5120 (GELU) |
| Vocabulary size | 25 |
| Positional encoding | Learned |
| Normalization | LayerNorm (embedding, pre-attention/pre-FFN, and final) |
| Architecture | ESM-1b-style pre-LN Transformer encoder |
| Max sequence length | 1024 total tokens (1022 nucleotides plus CLS/EOS) |
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.pthfrom 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. Hugging Face port maintained by Taykhoom Dalal.
License
MIT, following the original repository.