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esm2-650m-protein-localization-lora
rustambekurokov/esm2-650m-protein-localization-lora
esm2-650m-protein-localization-lora is a machine learning model from rustambekurokov. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as mit.
Fine-tuned ESM-2 650M with LoRA for predicting protein subcellular localization (10 classes).
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At a glance
- Library
- peft
- License
- mit
- Access
- Public
- Created
- Jul 8, 2026
- Updated
- Jul 8, 2026
- SHA
- 183b6b29
Base models
Datasets
Model card
From the Hugging Face model README
ESM-2 650M + LoRA for Protein Subcellular Localization
Fine-tuned ESM-2 650M with LoRA for predicting protein subcellular localization (10 classes).
Results
| Model | Params Trained | Accuracy | F1 (macro) | MCC |
|---|---|---|---|---|
| ESM-2 8M linear probe | 100% (head only) | 69.6% | 0.581 | 0.614 |
| ESM-2 35M full fine-tune | 100% | 74.3% | 0.647 | 0.677 |
| ESM-2 150M full fine-tune | 100% | 76.6% | 0.696 | 0.706 |
| ESM-2 650M LoRA | 2.4% | 76.5% | 0.668 | 0.704 |
Usage
import torch
from transformers import AutoTokenizer, EsmModel
from peft import LoraConfig, get_peft_model
# Load base model + LoRA weights
tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D")
# See full inference code in the repository
Labels
| ID | Location |
|---|---|
| 0 | Cytoplasm |
| 1 | Nucleus |
| 2 | Extracellular |
| 3 | Cell membrane |
| 4 | Mitochondrion |
| 5 | Endoplasmic reticulum |
| 6 | Membrane |
| 7 | Golgi apparatus |
| 8 | Lysosome/Vacuole |
| 9 | Peroxisome |
Training
- Dataset: DeepLoc 2.0 (17,266 train / 3,700 val / 3,701 test)
- LoRA config: r=16, alpha=32, target_modules=[query, key, value]
- Training: 10 epochs, lr=2e-4, batch_size=32, cosine schedule
- Hardware: NVIDIA DGX Spark (128GB unified memory)
Citation
@article{lin2023evolutionary,
title={Evolutionary-scale prediction of atomic-level protein structure with a language model},
author={Lin, Zeming and Akin, Halil and Rao, Roshan and others},
journal={Science},
year={2023}
}
- Library
- peft
- License
- mit
- Languages
- en
- Created
- Jul 8, 2026
- Updated
- Jul 8, 2026