GLaMM
Collection
Grounding Large Multimodal Model (GLaMM), the first-of-its-kind model capable of generating natural language responses that are seamlessly integrated. • 9 items • Updated • 4
👁️ GLaMM-GCG
📝 Description
GLaMM-GCG is the model specific to the Grounded Conversation Generation (GCG) task. It is finetuned of GranD-f dataset.
💻 Download
To get started with GLaMM-GCG, follow these steps:
git lfs install
git clone https://huggingface.co/MBZUAI/GLaMM-GCG
📚 Additional Resources
- Paper: ArXiv.
- GitHub Repository: For training and updates: GitHub - GLaMM.
- Project Page: For a detailed overview and insights into the project, visit our Project Page - GLaMM.
📜 Citations and Acknowledgments
@article{hanoona2023GLaMM,
title={GLaMM: Pixel Grounding Large Multimodal Model},
author={Rasheed, Hanoona and Maaz, Muhammad and Shaji, Sahal and Shaker, Abdelrahman and Khan, Salman and Cholakkal, Hisham and Anwer, Rao M. and Xing, Eric and Yang, Ming-Hsuan and Khan, Fahad S.},
journal={ArXiv 2311.03356},
year={2023}
}
- Downloads last month
- 776
Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "MBZUAI/GLaMM-GCG"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MBZUAI/GLaMM-GCG", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'