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dmis-lab
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Model Details
Model Provider
dmis-lab
Model Tree
Qwen/Qwen3-VL-8B-Instruct
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import Qwen3VLForConditionalGeneration, AutoProcessorfrom PIL import Imageimport torch # Load the model (MRPO Qwen3-VL checkpoint; or a local trained checkpoint path)model_path = "dmis-lab/Qwen3-VL-8B-Instruct-MRPO"model = Qwen3VLForConditionalGeneration.from_pretrained( model_path, torch_dtype=torch.bfloat16, device_map="auto",)processor = AutoProcessor.from_pretrained(model_path) # Example usage (no system prompt; Qwen3 uses <thinking> tags for reasoning)image_path = "path/to/medical/image.jpg"question = "What can you see in this medical image?" question_text = ( f"{question} Think step-by-step and enclose your reasoning in " "<thinking>...</thinking> tags. Then provide your answer in <answer>...</answer> tags.")messages = [ { "role": "user", "content": [ {"type": "image", "image": image_path}, {"type": "text", "text": question_text}, ], }] # Preparation for inferencetext = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True)inputs = processor( text=[text], images=[Image.open(image_path)], padding=True, padding_side="left", return_tensors="pt",)inputs = inputs.to(model.device) # Inference (greedy decoding, matching inference.py)generated_ids = model.generate(**inputs, use_cache=True, max_new_tokens=512, do_sample=False)generated_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)print(output_text)bibtex
@misc{jung2026breakingfailurecascadesstepaware, title={Breaking Failure Cascades: Step-Aware Reinforcement Learning for Medical Multimodal Reasoning}, author={Junha Jung and Minbyul Jeong and Suhyeon Lim and Sungwook Jung and Jaehoon Yun and Taeyun Roh and Mujeen Sung and Jaewoo Kang}, year={2026}, eprint={2606.31825}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2606.31825}, }This model is released under the Apache 2.0 license.
from transformers import Qwen3VLForConditionalGeneration, AutoProcessorfrom PIL import Imageimport torch # Load the model (MRPO Qwen3-VL checkpoint; or a local trained checkpoint path)model_path = "dmis-lab/Qwen3-VL-8B-Instruct-MRPO"model = Qwen3VLForConditionalGeneration.from_pretrained( model_path, torch_dtype=torch.bfloat16, device_map="auto",)processor = AutoProcessor.from_pretrained(model_path) # Example usage (no system prompt; Qwen3 uses <thinking> tags for reasoning)image_path = "path/to/medical/image.jpg"question = "What can you see in this medical image?" question_text = ( f"{question} Think step-by-step and enclose your reasoning in " "<thinking>...</thinking> tags. Then provide your answer in <answer>...</answer> tags.")messages = [ { "role": "user", "content": [ {"type": "image", "image": image_path}, {"type": "text", "text": question_text}, ], }] # Preparation for inferencetext = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True)inputs = processor( text=[text], images=[Image.open(image_path)], padding=True, padding_side="left", return_tensors="pt",)inputs = inputs.to(model.device) # Inference (greedy decoding, matching inference.py)generated_ids = model.generate(**inputs, use_cache=True, max_new_tokens=512, do_sample=False)generated_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)print(output_text)@misc{jung2026breakingfailurecascadesstepaware, title={Breaking Failure Cascades: Step-Aware Reinforcement Learning for Medical Multimodal Reasoning}, author={Junha Jung and Minbyul Jeong and Suhyeon Lim and Sungwook Jung and Jaehoon Yun and Taeyun Roh and Mujeen Sung and Jaewoo Kang}, year={2026}, eprint={2606.31825}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2606.31825}, }