Image-Text-to-Text
Transformers
Safetensors
English
qwen2_5_vl
gui
agent
gui-grounding
reinforcement-learning
conversational
text-generation-inference
Instructions to use InfiX-ai/InfiGUI-G1-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InfiX-ai/InfiGUI-G1-3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="InfiX-ai/InfiGUI-G1-3B") 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)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("InfiX-ai/InfiGUI-G1-3B") model = AutoModelForMultimodalLM.from_pretrained("InfiX-ai/InfiGUI-G1-3B") 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?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use InfiX-ai/InfiGUI-G1-3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "InfiX-ai/InfiGUI-G1-3B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "InfiX-ai/InfiGUI-G1-3B", "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/InfiX-ai/InfiGUI-G1-3B
- SGLang
How to use InfiX-ai/InfiGUI-G1-3B 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 "InfiX-ai/InfiGUI-G1-3B" \ --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": "InfiX-ai/InfiGUI-G1-3B", "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 "InfiX-ai/InfiGUI-G1-3B" \ --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": "InfiX-ai/InfiGUI-G1-3B", "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 InfiX-ai/InfiGUI-G1-3B with Docker Model Runner:
docker model run hf.co/InfiX-ai/InfiGUI-G1-3B
Improve model card: Add abstract and prominent GitHub link
#1
by nielsr HF Staff - opened
This PR enhances the InfiGUI-G1-3B model card by:
- Adding a prominent GitHub repository badge for improved discoverability.
- Including the full paper abstract in a dedicated "Paper Abstract" section.
- Refactoring the existing model-specific introduction into a "Model Description" section for clarity.
- Removing redundant paper and repository links at the end of the "Quick Start" section as they are now prominently displayed at the top.
These updates provide more comprehensive information about the model and its underlying research, making the model card more informative for users.
SiriusL changed pull request status to merged