tuandunghcmut/corgi-qwen3-vl-demo

CoRGI Qwen3-VL Demo

This Space showcases the CoRGI reasoning pipeline powered entirely by Qwen/Qwen3-VL-2B-Instruct. Upload an image, ask a visual question, and the app will:

1. 1.Generate structured reasoning steps with visual-verification flags.
2. 2.Request region-of-interest evidence for steps that require vision.
3. 3.Synthesize a grounded final answer.

Running Locally

pip install -r requirements.txt
python examples/demo_qwen_corgi.py \
  --model-id Qwen/Qwen3-VL-2B-Instruct \
  --max-steps 3 \
  --max-regions 3

To launch the Gradio demo locally:

python app.py

📚 Full Documentation

See [docs/](docs/) folder for complete documentation:

• —🚀 [Quick Start](docs/START_HERE.md) - Begin here!
• —📖 [Usage Guide](docs/USAGE_GUIDE.md) - How to use
• —🔧 [Deployment](docs/DEPLOY_NOW.md) - Deploy to HF Spaces
• —📊 [Summary Report](docs/SUMMARY_REPORT.md) - Full overview

Configuration Notes

• —Model: Uses Qwen/Qwen3-VL-2B-Instruct (2B parameters, ~5GB VRAM)
• —Single GPU: Model loads on single GPU (cuda:0) to avoid memory fragmentation
• —Hardware: The Space runs on cpu-basic tier by default
• —Customization: Set CORGI_QWEN_MODEL environment variable to use a different checkpoint
• —Sliders: max_steps and max_regions control reasoning depth and ROI candidates

UI Overview

• —Chain of Thought: Displays the structured reasoning steps with vision flags, alongside the exact prompt/response sent to the model.
• —ROI Extraction: Shows the source image with every grounded bounding box plus per-evidence crops, and lists the prompts used for each verification step.
• —Evidence Descriptions: Summarises each grounded region (bbox, description, confidence) with the associated ROI prompts.
• —Answer Synthesis: Highlights the final answer, supporting context, and the synthesis prompt/response pair.
• —Performance: Reports per-stage timings (reasoning, ROI extraction, synthesis) plus overall latency so you can monitor ZeroGPU runtime limits.