mRNA-FM
A 12-layer BERT-style transformer pre-trained on 45 million mRNA coding sequences (CDS) using codon (3-mer) tokenisation.
Architecture
| Parameter | Value |
|---|---|
| Layers | 12 |
| Attention heads | 20 |
| Embedding dimension | 1280 |
| FFN hidden dimension | 5120 (GELU) |
| Vocabulary size | 73 |
| Positional encoding | Learned |
| Normalization | LayerNorm (pre-attention/pre-FFN and final) |
| Architecture | ESM-1b-style pre-LN Transformer encoder |
| Max sequence length | 1024 total tokens (1022 codons = 3066 nucleotides, plus CLS/EOS) |
Vocabulary: <cls> (0), <pad> (1), <eos> (2), <unk> (3), 64 standard RNA codons
(indices 4-67), 4 null-padding tokens (68-71), <mask> (72).
Pretraining
- Objective: Masked language modelling (codon-level, 15% masking rate)
- Data: RefSeq -- 45 million mRNA coding sequences
- Source checkpoint:
mRNA-FM_pretrained.pthfrom cuhkaih/rnafm
Tokenisation
mRNA-FM uses codon (3-mer) tokenisation: the input sequence is split into consecutive non-overlapping codons (triplets) and each codon is mapped to a single token. The model therefore only accepts sequences whose length is a multiple of 3.
Input sequences must use RNA notation (U, not T). Convert before tokenising:
seq = seq.replace("T", "U")
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 (~3e-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 | Character tokenisation |
| mRNA-FM | 45 M CDS | 1280 | This model |
Usage
Embedding generation
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Taykhoom/mRNA-FM", trust_remote_code=True)
model = AutoModel.from_pretrained("Taykhoom/mRNA-FM", trust_remote_code=True)
model.eval()
# Sequences must be RNA (U not T) and length divisible by 3 (codons)
sequences = [
"AUGGGGUGCGAUCAUACCAGCACUAAUGCCCUCCUGGGAAGUCCUCGUGUUGCA",
"AUGCUAGCUAGCUAGCUAUG",
]
enc = tokenizer(sequences, return_tensors="pt", padding=True)
with torch.no_grad():
out = model(**enc)
cls_emb = out.last_hidden_state[:, 0, :] # (batch, 1280) -- CLS token
token_emb = out.last_hidden_state # (batch, n_codons+2, 1280) -- per-codon
# Intermediate layers
out_all = model(**enc, output_hidden_states=True)
layer6_emb = out_all.hidden_states[6]
CDS-aware embedding (mRNA sequences)
For mRNA sequences with a CDS track, use batch_encode_with_cds to apply T→U conversion,
extract only the coding region, chunk to codon boundaries, and encode — all in one call.
import numpy as np
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Taykhoom/mRNA-FM", trust_remote_code=True)
model = AutoModel.from_pretrained("Taykhoom/mRNA-FM", trust_remote_code=True)
model.eval()
# Binary CDS track: 1 at the first nucleotide of each codon in the CDS, 0 elsewhere
sequences = ["ATGCTAGCTAGCTAGCTATGCTAGCTAGCTAGCT"]
cds = [np.array([0]*5 + [1, 0, 0]*9 + [0]*2)] # example
enc, chunk_counts = tokenizer.batch_encode_with_cds(
sequences, cds, return_tensors="pt", padding=True, add_special_tokens=True
)
with torch.no_grad():
out = model(**enc)
# chunk_counts[i] = number of chunks produced for sequences[i]
# mean-pool non-special tokens for each sequence:
hidden = out.last_hidden_state # (total_chunks, seq_len, 1280)
MLM logits
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Taykhoom/mRNA-FM", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Taykhoom/mRNA-FM", trust_remote_code=True)
model.eval()
enc = tokenizer(["AUG<mask>GCUAUG"], return_tensors="pt")
with torch.no_grad():
logits = model(**enc).logits # (1, n_codons+2, 73)
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. Mean-pool over codon
positions (excluding CLS and EOS) for codon-level aggregation.
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.
Each codon token represents exactly one triplet of nucleotides. The tokeniser splits the raw sequence into non-overlapping codons; any trailing nucleotides that do not form a complete codon are silently discarded.
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.