Image-Text-to-Text
Transformers
3d-scene-understanding
scene-graph
multimodal
vlm
llama
vision-language-model
Instructions to use wingrune/3DGraphLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wingrune/3DGraphLLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="wingrune/3DGraphLLM")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("wingrune/3DGraphLLM", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use wingrune/3DGraphLLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "wingrune/3DGraphLLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "wingrune/3DGraphLLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/wingrune/3DGraphLLM
- SGLang
How to use wingrune/3DGraphLLM 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 "wingrune/3DGraphLLM" \ --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": "wingrune/3DGraphLLM", "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 "wingrune/3DGraphLLM" \ --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": "wingrune/3DGraphLLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use wingrune/3DGraphLLM with Docker Model Runner:
docker model run hf.co/wingrune/3DGraphLLM
Enhance model card for 3DGraphLLM with metadata, abstract, performance, and usage
#1
by nielsr HF Staff - opened
This PR significantly enhances the model card for 3DGraphLLM by:
- Adding
pipeline_tag: image-text-to-textto categorize the model's functionality for better discoverability on the Hub. - Adding
library_name: transformersto indicate compatibility with the Hugging Face Transformers library for programmatic use. - Including additional relevant
tagssuch as3d-scene-understanding,scene-graph,multimodal,vlm, andllama. - Adding the full paper abstract to provide a detailed overview of the model directly on the card.
- Updating the paper link to the official Hugging Face Papers page: 3DGraphLLM: Combining Semantic Graphs and Large Language Models for 3D Scene Understanding.
- Including a direct link to the official GitHub repository for code access.
- Incorporating the performance benchmark table from the GitHub README for quick assessment of the model's capabilities.
- Adding a "Usage" section that directs users to the comprehensive instructions on the GitHub repository and provides the demo command as a sample usage.
- Adding "Acknowledgement" and "Contact" sections from the GitHub README for completeness.
Please review and merge if these improvements are satisfactory.
wingrune changed pull request status to merged