README.md

---
language:
- rna
library_name: transformers
tags:
- RNA
- language-model
- UTR
- genomics
- biology
license: gpl-3.0
---

# UTR-LM-MLMSI

UTR-LM is a 5' UTR RNA language model based on ESM2, pretrained on endogenous 5' UTRs from five species and a large synthetic library. This checkpoint (`UTR-LM-MLMSI`) was trained with **MLM + minimum free energy (MFE) regression** as a supervised auxiliary objective.

## Architecture

| Parameter | Value |
|---|---|
| Layers | 6 |
| Attention heads | 16 |
| Embedding dimension | 128 |
| Vocabulary size | 10 |
| Positional encoding | Rotary (RoPE) |
| Architecture | ESM2-style pre-LN Transformer |

**Vocabulary:** `<pad>` (0), `<eos>` (1), `<unk>` (2), `A` (3), `G` (4), `C` (5), `T` (6), `<cls>` (7), `<mask>` (8), `<sep>` (9)

## Pretraining

- **Objective:** Masked language modeling + MFE (minimum free energy) regression
- **Data:** Endogenous 5' UTRs from five species (human, mouse, zebrafish, *Drosophila*, yeast) combined with the Cao et al. random 5' UTR synthetic library
- **Source checkpoint:** `ESM2SI_3.1_fiveSpeciesCao_6layers_16heads_128embedsize_4096batchToks_MLMLossMin.pkl`

### Checkpoint selection

Multiple `ESM2SI` checkpoints were available (versions 3.1, FS4.1, FS4.4, FS4.7). The `3.1` checkpoint was selected because it is the version specified in the original UTR-LM paper for translation efficiency (TE) and expression level (EL) downstream tasks (used in the `MJ4_Finetune` evaluation scripts). The FS4.x variants are later training runs but were not the ones reported in the original publication.

## Parity Verification

Hidden-state representations produced by this HF model are verified to be **exactly identical** (max absolute difference = 0.00) to the original ESM2-based implementation at all 7 representation levels (initial embedding + 6 transformer layers). Verified on GPU with PyTorch 2.8 / CUDA 12.6.

## Related Models

See the full [UTR-LM collection](https://huggingface.co/collections/Taykhoom/utr-lm-6a173a96ae7c070c3a84ebb4).

| Model | Pretraining Objective | Notes |
|---|---|---|
| [UTR-LM-MLM](https://huggingface.co/Taykhoom/UTR-LM-MLM) | MLM | Base model |
| **[UTR-LM-MLMSI](https://huggingface.co/Taykhoom/UTR-LM-MLMSI)** | MLM + MFE regression | This model — recommended for TE / EL tasks |
| [UTR-LM-MLMSS](https://huggingface.co/Taykhoom/UTR-LM-MLMSS) | MLM + secondary structure | — |
| [UTR-LM-MLMSISS](https://huggingface.co/Taykhoom/UTR-LM-MLMSISS) | MLM + MFE + secondary structure | Recommended for MRL tasks |

## Usage

### Embedding generation

```python
import torch
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("Taykhoom/UTR-LM-MLMSI", trust_remote_code=True)
model = AutoModel.from_pretrained("Taykhoom/UTR-LM-MLMSI", trust_remote_code=True)
model.eval()

sequences = ["ATGCATGCATGC", "GCTAGCTAGCTAGCTA"]
enc = tokenizer(sequences, return_tensors="pt", padding=True)

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

# CLS token embedding (position 0) - recommended for sequence-level tasks
cls_emb = out.last_hidden_state[:, 0, :]   # (batch, 128)

# All-token embeddings
token_emb = out.last_hidden_state           # (batch, seq_len, 128)

# Intermediate layer representations
out_all = model(**enc, output_hidden_states=True)
layer3_emb = out_all.hidden_states[3]       # after layer 3, shape (batch, seq_len, 128)
```

### MLM logits

```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("Taykhoom/UTR-LM-MLMSI", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Taykhoom/UTR-LM-MLMSI", trust_remote_code=True)
model.eval()

enc = tokenizer(["ATGC<mask>ATGC"], return_tensors="pt")
with torch.no_grad():
    logits = model(**enc).logits   # (1, seq_len, 10)
```

### Fine-tuning

The model follows standard HF conventions and can be fine-tuned with any Trainer-compatible setup. For sequence regression tasks, use the CLS token embedding as input to a prediction head (as done in the original UTR-LM paper).

## Citation

```bibtex
@article{chu2024_utrlm,
  title   = {A 5' {UTR} Language Model for Decoding Untranslated Regions of {mRNA} and Function Predictions},
  author  = {Chu, Yanyi and Yu, Dan and Li, Yupeng and Huang, Kaixuan and Shen, Yue and Cong, Le and Zhang, Jason and Wang, Mengdi},
  journal = {Nature Machine Intelligence},
  volume  = {6},
  number  = {4},
  pages   = {449--460},
  year    = {2024},
  doi     = {10.1038/s42256-024-00823-9}
}
```

## Implementation Notes

The original UTR-LM implementation uses standard scaled dot-product attention. This HF port adds support for `attn_implementation="sdpa"` (PyTorch `F.scaled_dot_product_attention`) and `attn_implementation="flash_attention_2"` (requires `pip install flash-attn --no-build-isolation`), which were not part of the original codebase.

## Credits

Original model and code by Yanyi Chu et al. (Stanford). Source code: [UTR-LM GitHub repository](https://github.com/a96123155/UTR-LM). The HF conversion code was authored primarily by [Claude Code](https://claude.ai/code) and reviewed manually by Taykhoom Dalal.

## License

GPL-3.0, following the original UTR-LM repository.