LLaVA++: Expanding Visual Capabilities with LLaMA-3 and Phi-3
Introduction
LLaVA++ is an ambitious project that enhances the existing LLaVA 1.5 model by incorporating the newest language learning models (LLMs) into its framework. These state-of-the-art models include the Phi-3 Mini Instruct 3.8B and LLaMA-3 Instruct 8B, both of which have been integrated to extend the model's visual and language processing capabilities. LLaVA++ aims to provide a more comprehensive tool for handling complex visual-language tasks.
Latest Updates
Recently, the team released exciting updates to LLaVA++, including new demos of the LLaMA-3-V and Phi-3-V models on Hugging Face Spaces. These updates offer users the opportunity to experiment with the enhanced capabilities of the models through an online demo, as well as in a Google Colab environment. The project has also introduced several finely-tuned models using techniques like LoRA and S2 fine-tuning, highlighting advancements in model performance and usability.
Project Outcomes
The integration of these models allows for an impressive comparison on various benchmarks, particularly those focusing on instruction-following and academic tasks. Visual representations, such as radar plots, underscore the improvements made in task-oriented datasets and show how LLaVA++ models stand out in performance evaluation metrics.
The Model Zoo
LLaVA++ hosts a diverse "model zoo," which serves as a repository of different versions of the models, each with unique training backgrounds and capabilities:
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Phi-3-V Models:
- The Phi-3 models range from pretrained versions to those tuned with LoRA and fully fine-tuned variations. Each offers distinct specialization depending on user needs, be it pretraining excellence or finely honed diversification.
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LLaMA-3 Models:
- Similarly, the LLaMA-3 suite includes pretrained models, those refined using LoRA techniques, and fully fine-tuned sets. These models cater to varied application scenarios and reflect comprehensive training on vast datasets.
Every model can be accessed via links to their respective Hugging Face pages, providing users with straightforward methods to explore and utilize these powerful tools.
Installation and Usage
For developers interested in using LLaVA++, installation is straightforward. The necessary scripts and dependencies are provided for seamless setup. Once installed, users can integrate either the Phi-3-V or LLaMA-3-V models by following a series of commands tailored for pre-training and fine-tuning, thus enabling them to adapt the technology to specific visual-language processing needs.
Acknowledgements
LLaVA++ has been built as an open-source project, benefiting tremendously from the contributions of the broader machine learning community. The project acknowledges significant reliance on existing models and frameworks, which have been instrumental in crafting a robust platform for exploring new frontiers in visual-language modeling.
Conclusion
LLaVA++ marks a significant step forward in the field of visual and language integrated models. With its enhanced models and fine-tuned architectures, the project not only sets new benchmarks in the field but also provides the machine learning community with easily accessible, high-performing tools. As developments continue, LLaVA++ is poised to become integral to cutting-edge visual-language applications.