- Q -VL: A VERSATILE V M FOR UNDERSTANDING, L ING AND EYOND QWEN-VL: A . . .
(iii) 3-stage training pipeline, and (iv) multilingual multimodal cleaned corpus Beyond the conventional image description and question-answering, we imple-ment the grounding and text-reading ability of Qwen-VLs by aligning image-caption-box tuples The resulting models, including Qwen-VL and Qwen-VL-Chat, set new records for generalist models under similar model scales on a broad range of
- Qwen2 Technical Report - OpenReview
This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models We release a comprehensive suite of foundational and instruction-tuned
- Qwen-VL: A Versatile Vision-Language Model for Understanding . . .
In this work, we introduce the Qwen-VL series, a set of large-scale vision-language models (LVLMs) designed to perceive and understand both texts and images Starting from the Qwen-LM as a
- LLaVA-MoD: Making LLaVA Tiny via MoE-Knowledge Distillation
Superior Performance: LLaVA-MoD surpasses larger models like Qwen-VLChat-7B in various benchmarks, demonstrating the effectiveness of its knowledge distillation approach
- Qwen2. 5 Technical Report - OpenReview
In this report, we introduce Qwen2 5, a comprehensive series of large language models (LLMs) designed to meet diverse needs Compared to previous iterations, Qwen 2 5 has been significantly
- Junyang Lin - OpenReview
Junyang Lin Pronouns: he him Principal Researcher, Qwen Team, Alibaba Group Joined July 2019
- MedJourney: Benchmark and Evaluation of Large Language Models over . . .
A1: Thank you for your insightful suggestion In our manuscript, we evaluated several public large language models (LLMs) such as ChatGLM3 and QWen, as well as specialized LLMs like HuatuoGPT2 and DISC-MedLLM, which are primarily Chinese LLMs We fully acknowledge your point about the broader applicability of our benchmark
- Alleviating Hallucination in Large Vision-Language Models with. . .
Despite the remarkable ability of large vision-language models (LVLMs) in image comprehension, these models frequently generate plausible yet factually incorrect responses, a phenomenon known as
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