📃 Paper | 🏠 Project | 🤗 Collection
## ✨ Model Summary **ZwZ2-27B** is the **Qwen3.6-27B**-based model in **Zooming-without-Zooming-v2 (ZwZ2)**. It learns to connect visual evidence across regions through comparison, aggregation, matching, and multi-step reasoning. Training uses full images; inference requires no zooming tools. On **25 benchmarks across six domains**, it improves over its base model on every benchmark, increasing overall accuracy from **55.52 to 60.51 (+4.99 points)**. Gains extend from fine-grained perception to spatial reasoning, long-context understanding, multimodal agents, and general visual reasoning. ## ⚙️ Method and Data **EvidenceConnect** builds verified multi-region questions from SA-1B images: 1. Group 3–7 related regions and generate questions using privileged zoomed views. 2. Check answers through cross-model agreement and drop-one region tests. 3. Convert verified questions to four-option tasks and train with **GRPO**, without an additional supervised fine-tuning stage. All three model sizes use approximately **20K training examples**. The policy sees full images with colored region outlines; privileged crop layouts are used only for data construction. See [ZwZ2-RL-VQA](https://huggingface.co/datasets/inclusionAI/ZwZ2-RL-VQA) for the data schema and synthesis process. ## 📊 Performance  [Editable vector PDF](assets/zwz2_performance_highlights_editable.pdf). The figure shows eight representative benchmarks; the complete results follow.  [Full-resolution results](assets/zwz2_27b_results.png). Scores are accuracies (%); domain and overall averages weight the corresponding benchmarks equally. **MMLongBench\*** uses the visual core: V-RAG, Image MM-NIAH, and VH-Multi, comprising **25 non-Counting tasks**. Advanced-model image resolution is constrained by their serving platforms. ## 🚀 Quickstart ### Installation Install a PyTorch build compatible with your hardware, then: ```bash pip install "transformers==5.13.1" accelerate pillow huggingface_hub ``` ### Inference ```python from pathlib import Path import torch from huggingface_hub import hf_hub_download from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration model_id = "inclusionAI/ZwZ2-27B" model = Qwen3_5ForConditionalGeneration.from_pretrained( model_id, dtype=torch.bfloat16, device_map="auto" ).eval() processor = AutoProcessor.from_pretrained(model_id) processor.chat_template = Path( hf_hub_download(model_id, "chat_template.jinja") ).read_text(encoding="utf-8") messages = [{"role": "user", "content": [ {"type": "image", "url": "/path/to/image.jpg"}, {"type": "text", "text": "Describe this image."}, ]}] inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt", enable_thinking=False, ).to(model.device) with torch.inference_mode(): generated_ids = model.generate( **inputs, max_new_tokens=2048, do_sample=False ) output_text = processor.batch_decode( generated_ids[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True, clean_up_tokenization_spaces=False, )[0] print(output_text) ``` Replace `/path/to/image.jpg` with an existing image. All three sizes use `Qwen3_5ForConditionalGeneration`, including the Qwen3.6-27B-based model. The example preserves the released chat template and uses **non-thinking mode**. The template requests final answers inside `