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- 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
Remarkably, LLaVA-MoD-2B surpasses Qwen-VL-Chat-7B with an average gain of 8 8\%, using merely $0 3\%$ of the training data and 23\% trainable parameters The results underscore LLaVA-MoD's ability to effectively distill comprehensive knowledge from its teacher model, paving the way for developing efficient MLLMs
- Q -VL: A VERSATILE V M FOR UNDERSTANDING, L ING AND EYOND QWEN-VL: A . . .
In this paper, we explore a way out and present the newest members of the open-sourced Qwen fam-ilies: Qwen-VL series Qwen-VLs are a series of highly performant and versatile vision-language foundation models based on Qwen-7B (Qwen, 2023) language model We empower the LLM base-ment with visual capacity by introducing a new visual receptor including a language-aligned visual encoder and a
- Instance-adaptive Zero-shot Chain-of-Thought Prompting
Experiments conducted with LLaMA-2, LLaMA-3, and Qwen on math, logic, and commonsense reasoning tasks (e g , GSM8K, MMLU, Causal Judgement) obtain consistent improvement, demonstrating that the instance-adaptive zero-shot CoT prompting performs better than other task-level methods with some curated prompts or sophisticated procedures, showing
- pi-Flow: Policy-Based Few-Step Generation via Imitation Distillation
Few-step diffusion or flow-based generative models typically distill a velocity-predicting teacher into a student that predicts a shortcut towards denoised data This format mismatch has led to
- Junyang Lin - OpenReview
Junyang Lin Pronouns: he him Principal Researcher, Qwen Team, Alibaba Group Joined July 2019
- ADIFF: Explaining audio difference using natural language
We evaluate our model using objective metrics and human evaluation and show our model enhancements lead to significant improvements in performance over naive baseline and SoTA Audio-Language Model (ALM) Qwen Audio
- MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context . . .
(a) Summary of Scientific Claims and Findings The paper presents MagicDec, a speculative decoding technique aimed at improving throughput and reducing latency for long-context Large Language Models (LLMs) It challenges the conventional understanding by demonstrating that speculative decoding can be effective even in high-throughput scenarios with large batch sizes and extended sequences
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