Image-Text-to-Text
Transformers
Safetensors
English
Chinese
groundinganything
text-generation
visual-grounding
object-detection
referring-expression-comprehension
pointing
ocr
document-layout
custom-code
diffusion-language-model
conversational
custom_code
Instructions to use GroundingPI/GroundAnything with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GroundingPI/GroundAnything with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="GroundingPI/GroundAnything", 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", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use GroundingPI/GroundAnything with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "GroundingPI/GroundAnything" # 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", "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
- SGLang
How to use GroundingPI/GroundAnything 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" \ --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", "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" \ --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", "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 with Docker Model Runner:
docker model run hf.co/GroundingPI/GroundAnything
Commit History
12 days ago
-
Publish model weights, paper figures, and complete model card (#1)
- Add model card title and related repository links (b44d8144920b89be341dcf9d592947f696ad9bb8) - Upload model files with empty README (ba4cb9a56590b15e2ef94e2b3d84be1ec6e39b0b) - Add paper figures, logos, and updated demonstration videos (e52f61cdfc1539e03d5ca4b3d38a073ba135ac2e) - Preserve original embedded demo cover (0f8e30894c3ca86378d01ae51ec69c217c78151b) - Document architecture, inference, evaluation, and paper results (f879290cf53cb4eba3e083d77c0f68c0fa9801c3) - Document architecture, inference, evaluation, and paper results (252a2efc380197280edd921dc6b240102dd50b6c) - Align model card and license metadata with retained Kimi K3 terms (74c5d461c0f59852b0c04ead2d4508078c83bbab) Co-authored-by: Qize Yu <Skywalker0410@users.noreply.huggingface.co>37f388f