README.md

metadata

title: CoRGI Qwen3-VL Demo
emoji: 🐶
colorFrom: indigo
colorTo: pink
sdk: gradio
sdk_version: 5.41.1
app_file: app.py
pinned: false
license: apache-2.0

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. Generate structured reasoning steps with visual-verification flags.
2. Request region-of-interest evidence for steps that require vision.
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/ folder for complete documentation:

• 🚀 Quick Start - Begin here!
• 📖 Usage Guide - How to use
• 🔧 Deployment - Deploy to HF Spaces
• 📊 Summary Report - 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.