---
license: isc
tags:
- biology
- code
- medical
---

# **TinyDNABERT**

## 🌟 Overview

**TinyDNABERT** is a specialized deep learning model designed for understanding the language of DNA and performing DNA sequence classification tasks. This model is a compact and efficient version of the **DNABERT** model, optimized to reduce memory usage while maintaining high performance. TinyDNABERT is particularly well-suited for tasks where computational efficiency and fast inference times are crucial.

This repository provides all the necessary scripts and configurations to fine-tune TinyDNABERT on various DNA-related tasks using **LoRA (Low-Rank Adaptation)** configurations, enabling efficient adaptation to specific DNA sequence classification problems.

🚀 **Key Features:**
- **Compact & Efficient:** Smaller memory footprint with fast inference times.
- **LoRA Fine-Tuning:** Leverage Low-Rank Adaptation for quick and effective model tuning.
- **Task-Specific Adaptability:** Fine-tune the model for various DNA-related tasks with ease.

Please Cite As:

@misc{peerzada_fabiha_akmal_makhdoomi_2024,  
    author       = {Peerzada Fabiha Akmal Makhdoomi, Nimisha Ghosh},  
    title        = {TinyDNABERT},  
    year         = 2024,  
    url          = {https://huggingface.co/fabihamakhdoomi/TinyDNABERT},  
    doi          = {10.57967/hf/2886},  
    publisher    = {Hugging Face}  
}