Instructions to use MBZUAI/GLaMM-GranD-Pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MBZUAI/GLaMM-GranD-Pretrained with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MBZUAI/GLaMM-GranD-Pretrained")

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("MBZUAI/GLaMM-GranD-Pretrained")
model = AutoModelForCausalLM.from_pretrained("MBZUAI/GLaMM-GranD-Pretrained")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MBZUAI/GLaMM-GranD-Pretrained with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MBZUAI/GLaMM-GranD-Pretrained"
# 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-GranD-Pretrained",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MBZUAI/GLaMM-GranD-Pretrained

SGLang

How to use MBZUAI/GLaMM-GranD-Pretrained 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-GranD-Pretrained" \
    --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-GranD-Pretrained",
		"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-GranD-Pretrained" \
        --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-GranD-Pretrained",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MBZUAI/GLaMM-GranD-Pretrained with Docker Model Runner:
```
docker model run hf.co/MBZUAI/GLaMM-GranD-Pretrained
```

👁️ GLaMM-GranD-Pretrained

📝 Description

GLaMM-GranD-Pretrained is the model pretrained on GranD dataset, a large-scale dataset generated with automated annotation pipeline for detailed region-level understanding and segmentation masks. GranD comprises 7.5M unique concepts anchored in a total of 810M regions, each with a segmentation mask.

💻 Download

To get started with GLaMM-GranD-Pretrained, follow these steps:

git lfs install
git clone https://huggingface.co/MBZUAI/GLaMM-GranD-Pretrained

📚 Additional Resources

Paper: ArXiv.
GitHub Repository: For 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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Collection including MBZUAI/GLaMM-GranD-Pretrained

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 Jun 11, 2024 • 4

Paper for MBZUAI/GLaMM-GranD-Pretrained

GLaMM: Pixel Grounding Large Multimodal Model

Paper • 2311.03356 • Published Nov 6, 2023 • 36