Image-Text-to-Text
Transformers
Safetensors
English
Chinese
groundinganything_vlm
text-generation
visual-grounding
object-detection
referring-expression-comprehension
pointing
ocr
document-layout
custom-code
conversational
custom_code
Instructions to use GroundingPI/GroundAnything-VLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GroundingPI/GroundAnything-VLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="GroundingPI/GroundAnything-VLM", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("GroundingPI/GroundAnything-VLM", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use GroundingPI/GroundAnything-VLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "GroundingPI/GroundAnything-VLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GroundingPI/GroundAnything-VLM", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/GroundingPI/GroundAnything-VLM
- SGLang
How to use GroundingPI/GroundAnything-VLM 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 "GroundingPI/GroundAnything-VLM" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GroundingPI/GroundAnything-VLM", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "GroundingPI/GroundAnything-VLM" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GroundingPI/GroundAnything-VLM", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use GroundingPI/GroundAnything-VLM with Docker Model Runner:
docker model run hf.co/GroundingPI/GroundAnything-VLM
Commit History
12 days ago
-
Publish model weights, paper figures, and complete model card (#1)
- Add model card title and related repository links (f14f10d9061e50415d8b0a1ec2c06a52e69a4295) - Upload model files with empty README (c547b2097fba505624ae4e119eb3d50db9153981) - Add paper figures, logos, and updated demonstration videos (b23fec7b82e2205b8cb56c48bf05fd75388fdbec) - Preserve original embedded demo cover (c367acf62cc8255782e3d19bd8c312f9b7ab35db) - Document architecture, inference, evaluation, and paper results (ca15e4cfa8854b6c42b2e355faf19fe951e64dae) - Document architecture, inference, evaluation, and paper results (e0a1aad22a71952ba7e98dbe901f3033248d3e55) - Align model card and license metadata with retained Kimi K3 terms (719af50dac827c6571533029e46e4bc6d55c99e7) Co-authored-by: Qize Yu <Skywalker0410@users.noreply.huggingface.co>d603a32