File size: 1,747 Bytes
7bfcb56 9b9abe6 7bfcb56 9b9abe6 7bfcb56 9b9abe6 7bfcb56 9b9abe6 7bfcb56 9b9abe6 7bfcb56 9b9abe6 7bfcb56 9b9abe6 7bfcb56 9b9abe6 7bfcb56 9b9abe6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 |
---
library_name: transformers
license: mit
---
# Model Card for GERM-NT1
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** Haozheng Luo, ChengHao Qiu
- **License:** MIT
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/MAGICS-LAB/GERM
- **Paper:** https://arxiv.org/abs/2505.00598
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
```
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("magicslabnu/GERM-NT-2.5B-multi", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("magicslabnu/GERM-NT-2.5B-multi", trust_remote_code=True)
```
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
GLUE
**BibTeX:**
```
@misc{luo2025fastlowcostgenomicfoundation,
title={Fast and Low-Cost Genomic Foundation Models via Outlier Removal},
author={Haozheng Luo and Chenghao Qiu and Maojiang Su and Zhihan Zhou and Zoe Mehta and Guo Ye and Jerry Yao-Chieh Hu and Han Liu},
year={2025},
eprint={2505.00598},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2505.00598},
}
```
|