# Introduction

AIDO.ModelGenerator is a software stack powering the development of an AI-driven Digital Organism by enabling researchers to adapt pretrained models and generate finetuned models for downstream tasks.
To read more about AIDO.ModelGenerator's integral role in building the world's first AI-driven Digital Organism, see [AIDO](https://github.com/genbio-ai/AIDO).

AIDO.ModelGenerator is open-sourced as an opinionated plug-and-play research framework for cross-disciplinary teams in ML & Bio. 
It is designed to enable rapid and reproducible prototyping with four kinds of experiments in mind:

1. Applying pre-trained foundation models to new data
2. Developing new finetuning and inference tasks for foundation models
3. Benchmarking foundation models and creating leaderboards
4. Testing new architectures for finetuning performance

while also scaling with hardware and integrating with larger data pipelines or research workflows.

AIDO.ModelGenerator is built on PyTorch, HuggingFace, and Lightning, and works seamlessly with these ecosystems.

## Who uses AIDO.ModelGenerator?

### 🧬 Biologists 
* Intuitive one-command CLIs for in silico experiments
* Pre-trained model zoo
* Broad data compatibility
* Pipeline-oriented workflows

### 🤖 ML Researchers 
* Reproducible-by-design experiments
* Architecture A/B testing
* Automatic hardware scaling
* Integration with PyTorch, Lightning, HuggingFace, and WandB

### ☕ Software Engineers
* Extensible and modular models, tasks, and data
* Strict typing and documentation
* Fail-fast interface design
* Continuous integration and testing

### 🤝 Everyone benefits from
* A collaborative hub and focal point for multidisciplinary work on experiments, models, software, and data
* Community-driven development
* Permissive license for academic and non-commercial use

## Projects using AIDO.ModelGenerator

- [Accurate and General DNA Representations Emerge from Genome Foundation Models at Scale](https://doi.org/10.1101/2024.12.01.625444)
- [A Large-Scale Foundation Model for RNA Function and Structure Prediction](https://doi.org/10.1101/2024.11.28.625345)
- [Mixture of Experts Enable Efficient and Effective Protein Understanding and Design](https://doi.org/10.1101/2024.11.29.625425)
- [Scaling Dense Representations for Single Cell with Transcriptome-Scale Context](https://doi.org/10.1101/2024.11.28.625303)
- [Balancing Locality and Reconstruction in Protein Structure Tokenizer](https://doi.org/10.1101/2024.12.02.626366)
