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Textul Original
QVQ-72B-Preview has achieved remarkable performance on various benchmarks. It scored a remarkable 70.3% on the Multimodal Massive Multi-task Understanding (MMMU) benchmark, showcasing QVQ's powerful ability in multidisciplinary understanding and reasoning. Furthermore, the significant improvements on MathVision highlight the model's progress in mathematical reasoning tasks. OlympiadBench also demonstrates the model's enhanced ability to tackle challenging problems. But It's Not All Perfect: Acknowledging the Limitations While QVQ-72B-Preview exhibits promising performance that surpasses expectations, it’s important to acknowledge several limitations: Language Mixing and Code-Switching: The model might occasionally mix different languages or unexpectedly switch between them, potentially affecting the clarity of its responses. Recursive Reasoning Loops: There's a risk of the model getting caught in recursive reasoning loops, leading to lengthy responses that may not even arrive at a final answer. Safety and Ethical Considerations: Robust safety measures are needed to ensure reliable and safe performance. Users should exercise caution when deploying this model. Performance and Benchmark Limitations: Despite the improvements in visual reasoning, QVQ doesn’t entirely replace the capabilities of Qwen2-VL-72B. During multi-step visual reasoning, the model might gradually lose focus on the image content, leading to hallucinations. Moreover, QVQ doesn’t show significant improvement over Qwen2-VL-72B in basic recognition tasks like identifying people, animals, or plants. Note: Currently, the model only supports single-round dialogues and image outputs. It does not support video inputs.
Varianta Standard
QVQ-72B-Preview has demonstrated impressive performance across a range of benchmarks. It achieved a notable score of 70.3% on the Multimodal Massive Multi-task Understanding (MMMU) benchmark, highlighting QVQ's strong capabilities in interdisciplinary understanding and reasoning. Additionally, the substantial improvements observed on MathVision indicate the model's advancement in mathematical reasoning tasks. OlympiadBench also reflects the model's enhanced capacity to address complex problems. However, Not Everything is Ideal: Recognizing the Limitations Although QVQ-72B-Preview shows promising results that exceed expectations, it is essential to recognize several limitations: Language Mixing and Code-Switching: The model may sporadically mix languages or switch between them unexpectedly, which could compromise the clarity of its responses. Recursive Reasoning Loops: There is a possibility of the model becoming ensnared in recursive reasoning loops, resulting in prolonged responses that may not reach a definitive conclusion. Safety and Ethical Considerations: Strong safety protocols are necessary to ensure consistent and secure performance. Users should exercise caution when utilizing this model. Performance and Benchmark Limitations: Despite advancements in visual reasoning, QVQ does not entirely replace the functions of Qwen2-VL-72B. During multi-step visual reasoning tasks, the model may gradually lose focus on the image details, leading to inaccuracies. Furthermore, QVQ has not demonstrated significant enhancements over Qwen2-VL-72B in fundamental recognition tasks, such as identifying people, animals, or plants. Note: At this time, the model only accommodates single-round dialogues and image outputs, and it does not support video inputs.