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
How to use from
SGLangUse Docker images
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "MBZUAI/GLaMM-RefSeg" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "MBZUAI/GLaMM-RefSeg",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
👁️ GLaMM-RefSeg
📝 Description
GLaMM-RegCap-VG is the model specific to referring expression segmentation. "RefSeg" denotes its focus on segmentation tasks related to referring expressions.
💻 Download
To get started with GLaMM-RefSeg, follow these steps:
git lfs install
git clone https://huggingface.co/MBZUAI/GLaMM-RefSeg
📚 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}
}
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Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "MBZUAI/GLaMM-RefSeg" \ --host 0.0.0.0 \ --port 30000# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MBZUAI/GLaMM-RefSeg", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'