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- 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
- 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
- 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
- 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
- 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
- Junyang Lin - OpenReview
Junyang Lin Pronouns: he him Principal Researcher, Qwen Team, Alibaba Group Joined July 2019
- MagicDec: Breaking the Latency-Throughput Tradeoff for . . . - OpenReview
We have added evaluations for Mistral and Qwen series models to show the trends seen for Llama models also translate to the former (Appendix A 5, Page 15) MagicDec achieves impressive speedups for Mistral-7B-v0 3, Qwen-2 5-7B and Qwen2 5-32B even at large batch sizes
- 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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