• Diverse Model Support: Llama3, Phi, Mistral, Gemma, and more.
• Versatile Image Encoding: CLIP, Seglip, RADIO, and others.
• Customization Made Simple: YAML files and CLI for adaptability.
• Efficient Resource Utilization: Seamless operation on a single GPU.
• Seamless Deployment: Docker locally or on cloud with Skypilot.
• Comprehensive Documentation: Includes datasets for successful implementation.

1. Introduction
2. Supported_Models
3. Changelog
4. Installation
5. Pretrain
6. Finetune
7. Evaluate
8. Inference
9. Features to be Added
10. Citation
11. Acknowledgement

• Llama3
• Phi
• Mistral
• Gemma

• Version 1.0.1:
  • Added support for distributed training.
  • Included accelerate library.
• Version 1.0.0:
  • Initial release.

1. Clone the repository from GitHub.
2. Install dependencies using pip: pip install -r requirements.txt.
3. Run setup.sh to set up the environment.
4. Start using Eagle!

• Utilize supported models for pretraining multimodal models.

• Fine-tune pretrained models with custom datasets or tasks.

• Evaluate model performance using specified metrics and datasets.

• Deploy models for inference on new data or integrate them into existing systems.

• Add support for accelerate.
• Add support for additional Huggingface models such as falcon, mpt.

@article{AdithyaSKolavi2024,
  title={Eagle: Unified Platform to train multimodal models},
  author={Adithya S Kolavi},
  year={2024},
  url={https://github.com/adithya-s-k/eagle}
}

We would like to express our gratitude to the creators of LLaVA (Large Language and Vision Assistant) for providing the groundwork for our project. Visit their repository here.