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.pth from 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

python

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.

python

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

python

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

bibtex

@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.