Instructions to use MBZUAI/GLaMM-RefSeg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MBZUAI/GLaMM-RefSeg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MBZUAI/GLaMM-RefSeg")# Load model directly from transformers import AutoProcessor, AutoModelForCausalLM processor = AutoProcessor.from_pretrained("MBZUAI/GLaMM-RefSeg") model = AutoModelForCausalLM.from_pretrained("MBZUAI/GLaMM-RefSeg") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use MBZUAI/GLaMM-RefSeg with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MBZUAI/GLaMM-RefSeg" # 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-RefSeg", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MBZUAI/GLaMM-RefSeg
- SGLang
How to use MBZUAI/GLaMM-RefSeg with SGLang:
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 }'Use 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 }' - Docker Model Runner
How to use MBZUAI/GLaMM-RefSeg with Docker Model Runner:
docker model run hf.co/MBZUAI/GLaMM-RefSeg
Update README.md
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README.md
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license: apache-2.0
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license: apache-2.0
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# ๐๏ธ GLaMM-RefSeg
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---
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## ๐ Description
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GLaMM-RegCap-VG is the model specific to referring expression segmentation. "RefSeg" denotes its focus on segmentation tasks related to referring expressions.
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## ๐ป Download
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To get started with GLaMM-FullScope, follow these steps:
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```
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git lfs install
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git clone https://huggingface.co/MBZUAI/GLaMM-RefSeg
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```
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## ๐ Additional Resources
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- **GitHub Repository:** For training and updates: [GitHub - GLaMM](https://github.com/mbzuai-oryx/groundingLMM).
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- **Project Page:** For a detailed overview and insights into the project, visit our [Project Page - GLaMM](https://mbzuai-oryx.github.io/groundingLMM/).
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## ๐ Citations and Acknowledgments
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```bibtex
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@article{hanoona2023GLaMM,
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title={GLaMM: Pixel Grounding Large Multimodal Model},
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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.},
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journal={ArXiv 2311.03356},
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year={2023}
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}
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