ERNIE-RNA

ERNIE-RNA is an RNA-specific large language model that incorporates RNA base-pairing potential as a recurrent 2D structural bias into each attention layer, enabling the model to capture secondary structure information during pretraining.

Architecture

Vocabulary

2D RNA Pairing Bias

ERNIE-RNA computes a pairwise RNA base-pairing potential matrix from the input sequence at the start of each forward pass. This matrix (shape [B, T, T, 1]) is projected to [B, H, T, T] via a 2-layer MLP (1 -> 6 -> H, with GELU) and added to the attention logits in the first layer. The pre-softmax attention scores then become the updated 2D bias for the next layer, creating a recurrent structural information pathway across all 12 transformer layers.

Base-pairing scores: A-U = 2.0, G-C = 3.0, G-U wobble = 0.8.

Pretraining

• Objective: Masked language modeling (MLM) on RNA sequences
• Data: RNAcentral (non-redundant RNA sequences)
• Source checkpoint: ERNIE-RNA_pretrain.pt

Checkpoint selection

Single pretrained checkpoint from the original repository. Used as-is; no fine-tuned variants are included in this release.

Parity Verification

Hidden-state representations verified identical (max abs diff = 1.82e-06) to the original implementation at all 13 representation levels (embedding + 12 transformer layers). Verified on GPU with PyTorch 2.7 / CUDA 12.

Only attn_implementation="eager" is supported (see Implementation Notes).

Related Models

See the full ERNIE-RNA collection.

Usage

Embedding generation

import torch
from transformers import AutoTokenizer, AutoModel

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

sequences = ["AUGCAUGCAUGC", "GGGGCCCCGGGG"]
enc = tokenizer(sequences, return_tensors="pt", padding=True)

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

cls_emb   = out.last_hidden_state[:, 0, :]   # (batch, 768) -- CLS token
token_emb = out.last_hidden_state             # (batch, seq_len, 768)

# Intermediate layers
out_all = model(**enc, output_hidden_states=True)
layer6_emb = out_all.hidden_states[6]         # (batch, seq_len, 768)

MLM logits

import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM

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

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

Fine-tuning

Use the CLS token embedding (last_hidden_state[:, 0, :]) as input to a prediction head for sequence-level tasks. For token-level tasks, use last_hidden_state directly.

Implementation Notes

ERNIE-RNA's recurrent 2D bias is updated from the pre-softmax attention scores at every layer (the raw QK logits become the bias input for the next layer). Fused attention kernels (SDPA, FlashAttention) do not expose pre-softmax scores, so they cannot maintain this recurrent pathway. Only attn_implementation="eager" is supported; requesting sdpa or flash_attention_2 raises a ValueError.

The twod_proj MLP is always run in float32 (matching the original) regardless of the model's compute dtype.

Citation

@article{yin2025_ernierna,
  title   = {{ERNIE-RNA}: an {RNA} language model with structure-enhanced representations},
  author  = {Yin, Weijie and Zhang, Zhaoyu and Zhang, Shuo and He, Liang and Zhang, Ruiyang and Jiang, Rui and Liu, Gan and Wang, Jingyi and Zhang, Xuegong and Qin, Tao and Xie, Zhen},
  journal = {Nature Communications},
  volume  = {16},
  number  = {1},
  pages   = {8407},
  year    = {2025},
  doi     = {10.1038/s41467-025-64972-0}
}

Credits

Original model and code by Yin et al. Source: GitHub. Hugging Face port maintained by Taykhoom Dalal.

License

MIT, following the original repository.