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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("wingrune/3DGraphLLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
Commit History
-
Remove library name
Verified signatureIt doesn't look like this model is compatible with the Transformers library.8848a4f
-
Enhance model card for 3DGraphLLM with metadata, abstract, performance, and usage (#1)
Verified signature- Enhance model card for 3DGraphLLM with metadata, abstract, performance, and usage (29402d8bd9597fdbce2218ff42f1a12c0d66dbc6) Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>50e9f64
-
Updated weights
ae08151
-
Upload 3dgraphllm.pth with huggingface_hub
Verified signaturee5a07bd -
Update README.md
Verified signatured3fc72d -
Update README.md
Verified signature4b9223c -
Upload ga.png
Verified signature35ac252 -
initial commit
Verified signaturec9e49bb