LLaVA++: Extending Visual Capabilities with LLaMA-3 and Phi-3

Hanoona Rasheed*, Muhammad Maaz*, Salman Khan, and Fahad Khan

* Equal contributions

Mohamed bin Zayed University of AI (MBZUAI)

📢 Latest Updates

• Apr-30-24- LLaMA-3-V and Phi-3-V demos are now available via Hugging Face Spaces. Check them out at LLaMA-3-V & Phi-3-V 🔥🔥🔥
• Apr-28-24- Online demo of Phi-3-V and LLaMA-3-V are released, check them out at Online Demo 🔥🔥🔥
• Apr-28-24- LoRA, fully fine-tuned and S² fine-tuned models and results are added! 🔥🔥🔥
• Apr-27-24- Google Colab is released to chat with Phi-3-V-3.8B model, check it out at Google Colab 🔥🔥🔥
• Apr-26-24- Phi-3-V and LLaVA-3-V released: Excited to release the new integration of LLaVA with Phi-3 Mini Instruct and LLaMA-3 Instruct models! Hugging Face 🔥🔥🔥

💬 Introduction

This repository enhances the capabilities of the LLaVA 1.5 model, incorporating latest LLMs released this weak🔥, Phi-3 Mini Instruct 3.8B, and LLaMA-3 Instruct 8B.

🏆 Results: Phi-3-V and LLaVA-3-V

Comparison on Benchmarks for Instruction-following LMMS & academic-task-oriented datasets:

• Average computed excluding MME, and second-best are underlined.

🤖 Model-Zoo

The following table provides an overview of the available models in our zoo. For each model, you can find links to its Hugging Face page.

Installation

git clone https://github.com/mbzuai-oryx/LLaVA-pp.git
cd LLaVA-pp
git submodule update --init --recursive

Packages you need to update from LLAVA:

pip install git+https://github.com/huggingface/transformers@a98c41798cf6ed99e1ff17e3792d6e06a2ff2ff3

🚀 Phi-3-V

To integrate Phi-3-V with LLaVA, follow these steps to update the codebase:

# Copy necessary files
cp Phi-3-V/train.py LLaVA/llava/train/train.py
cp Phi-3-V/llava_phi3.py LLaVA/llava/model/language_model/llava_phi3.py
cp Phi-3-V/builder.py LLaVA/llava/model/builder.py
cp Phi-3-V/model__init__.py LLaVA/llava/model/__init__.py
cp Phi-3-V/main__init__.py LLaVA/llava/__init__.py
cp Phi-3-V/conversation.py LLaVA/llava/conversation.py

# Training commands
cp scripts/Phi3-V_pretrain.sh LLaVA/Vi-phi3_pretrain.sh
cp scripts/Phi3-V_finetune_lora.sh LLaVA/Vi-phi3_finetune_lora.sh

Train Phi-3-V

1. Pre-train

cd LLaVA
bash Phi3-V_pretrain.sh

2. Finetune

cd LLaVA
bash Phi3-V_finetune_lora.sh

🚀 LLaMA-3-V

To integrate LLaMA-3-V with LLaVA, follow these steps to update the codebase:

# Copy necessary files
cp LLaMA-3-V/train.py LLaVA/llava/train/train.py
cp LLaMA-3-V/conversation.py LLaVA/llava/conversation.py
cp LLaMA-3-V/builder.py LLaVA/llava/model/builder.py
cp LLaMA-3-V/llava_llama.py LLaVA/llava/model/language_model/llava_llama.py

# Training commands
cp scripts/LLaMA3-V_pretrain.sh LLaVA/LLaMA3-V_pretrain.sh
cp scripts/LLaMA3-V_finetune_lora.sh LLaVA/LLaMA3-V_finetune_lora.sh

Train LLaMA-3-V

1. Pre-train

cd LLaVA
bash LLaMA3-V_pretrain.sh

2. Finetune

cd LLaVA
bash LLaMA3-V_finetune_lora.sh

🙏 Acknowledgement

We are thankful to LLaVA, lmms-eval and S²-Wrapper for releasing their models and code as open-source contributions.

In case if you face any issues or have any questions, please feel free to create an issue or reach out at [email protected] & [email protected].

📜 Citation

  @misc{hanoona2024LLaVA++,
          title={LLaVA++: Extending Visual Capabilities with LLaMA-3 and Phi-3},
          author={Rasheed, Hanoona and Maaz, Muhammad and Khan, Salman and Khan, Fahad S.},
          url={https://github.com/mbzuai-oryx/LLaVA-pp},
          year={2024}
  }