Video → Q&A (Qwen2.5-VL-7B WolfV2)

This Space lets you drag-and-drop a video and ask questions about it using Efficient-Large-Model/qwen2_5vl-7b-wolfv2-tuned (a fine-tuned Qwen2.5-VL-7B-Instruct).

Deploy

1. Create a new Hugging Face Space (Python + Gradio).
2. Add the three files from this repo: app.py, requirements.txt, README.md.
3. (Optional) In the Space Settings → Variables, set MODEL_ID=Efficient-Large-Model/qwen2_5vl-7b-wolfv2-tuned (default already).
4. (Optional) If your GPU VRAM is tight, set env var USE_INT4=1 to enable 4-bit weight-only quantization.

GPU recommended. A10/A100 or ZeroGPU works for short videos; longer/high-res videos may OOM on CPU.

How it works

• We construct a chat-style prompt with a video item and your question, then call processor.apply_chat_template(..., fps=1) and model.generate(...).
• You can increase fps for more temporal detail. Higher fps → more tokens/VRAM.
• Resolution bounds are controlled via min_pixels/max_pixels on AutoProcessor.

Tips

• If you see KeyError: 'qwen2_5_vl', your Transformers is too old — upgrade to >=4.50.0.
• If decoding fails for certain containers, try converting to .mp4 (H.264 + AAC).
• To return 5 QA pairs automatically, leave the question blank — the app uses a default instruction to summarize and produce 5 QAs.

Acknowledgments

• Model: Efficient-Large-Model/qwen2_5vl-7b-wolfv2-tuned
• Base architecture & usage patterns: Qwen/Qwen2.5-VL-7B-Instruct via 🤗 Transformers.