Instructions to use MBZUAI/GLaMM-GCG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MBZUAI/GLaMM-GCG with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MBZUAI/GLaMM-GCG")# Load model directly from transformers import AutoProcessor, AutoModelForCausalLM processor = AutoProcessor.from_pretrained("MBZUAI/GLaMM-GCG") model = AutoModelForCausalLM.from_pretrained("MBZUAI/GLaMM-GCG") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use MBZUAI/GLaMM-GCG with vLLM:
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 }'Use Docker
docker model run hf.co/MBZUAI/GLaMM-GCG
- SGLang
How to use MBZUAI/GLaMM-GCG 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-GCG" \ --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-GCG", "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-GCG" \ --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-GCG", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MBZUAI/GLaMM-GCG with Docker Model Runner:
docker model run hf.co/MBZUAI/GLaMM-GCG
| license: apache-2.0 | |
| # ποΈ GLaMM-GCG | |
| --- | |
| ## π Description | |
| GLaMM-GCG is the model specific to the Grounded Conversation Generation (GCG) task. It is finetuned of GranD-f dataset. | |
| ## π» Download | |
| To get started with GLaMM-GCG, follow these steps: | |
| ``` | |
| git lfs install | |
| git clone https://huggingface.co/MBZUAI/GLaMM-GCG | |
| ``` | |
| ## π Additional Resources | |
| - **Paper:** [ArXiv](https://arxiv.org/abs/2311.03356). | |
| - **GitHub Repository:** For training and updates: [GitHub - GLaMM](https://github.com/mbzuai-oryx/groundingLMM). | |
| - **Project Page:** For a detailed overview and insights into the project, visit our [Project Page - GLaMM](https://mbzuai-oryx.github.io/groundingLMM/). | |
| ## π Citations and Acknowledgments | |
| ```bibtex | |
| @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} | |
| } | |