Qwen3-VL
by
QwenLM

Description: Qwen3-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.

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Summary Information

Updated 22 minutes ago
Added to GitGenius on February 6th, 2025
Created on August 29th, 2024
Open Issues & Pull Requests: 423 (+0)
Number of forks: 1,817
Total Stargazers: 19,564 (+0)
Total Subscribers: 92 (+0)

Issue Activity (beta)

Open issues: 388
New in 7 days: 2
Closed in 7 days: 0
Avg open age: 114 days
Stale 30+ days: 374
Stale 90+ days: 350

Recent activity

Opened in 7 days: 1
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Repository Insights (GitGenius)

Median issue/PR response: 23.7 hours
Mean response time: 36.0 days
90th percentile: 137.9 days
Tracked items: 1,731

Most active contributors

Detailed Description

Qwen3-VL is a multimodal large language model series developed by the Qwen team at Alibaba Cloud that combines vision and language understanding capabilities. The repository, written primarily in Jupyter Notebook format, serves as the official codebase and documentation hub for this vision-language model family. According to GitGenius tracking, the repository has grown modestly with 19,528 stargazers and 1,808 forks as of the most recent check, with median issue and pull request response latency of 23.7 hours across 1,731 tracked items.

The Qwen3-VL series represents a significant advancement in multimodal AI, delivering comprehensive upgrades across visual perception, text understanding, reasoning capabilities, and context length. The models are available in both Dense and Mixture-of-Experts architectures that scale from edge devices to cloud deployments, with Instruct and reasoning-enhanced Thinking editions for flexible deployment scenarios. The repository includes releases spanning multiple model sizes, including the Qwen3-VL-2B, 4B, 8B, 32B, and 235B-A22B variants, alongside earlier Qwen2.5-VL and Qwen2-VL series models with various quantization options such as AWQ, GPTQ-Int4, and GPTQ-Int8.

Key technical innovations documented in the repository include Interleaved-MRoPE for enhanced positional embeddings across temporal and spatial dimensions, DeepStack architecture for fusing multi-level Vision Transformer features, and Text-Timestamp Alignment for precise video temporal modeling. The models demonstrate particular strengths in visual agent capabilities for operating PC and mobile GUIs, visual coding generation from images and videos, advanced spatial perception including 2D and 3D grounding, and extended context handling with native 256K expandable to 1M tokens. The repository highlights expanded optical character recognition supporting 32 languages with improved robustness in challenging conditions, and enhanced multimodal reasoning particularly for STEM and mathematical tasks.

The most active contributors tracked by GitGenius are ShuaiBai623 with 1,151 events, JJJYmmm with 428 events, and wulipc with 203 events. The repository maintains connections with related projects including vllm-project/vllm, paddlepaddle/paddleocr, and hiyouga/llamafactory through overlapping contributor networks. The codebase includes cookbooks covering recognition, localization, document parsing, video understanding, and key information extraction capabilities. The repository provides access to models through Hugging Face and ModelScope collections, alongside an interactive demo, API documentation, and a technical paper detailing the architecture and training methodology behind Qwen3-VL.

Qwen3-VL
by
QwenLMQwenLM/Qwen3-VL

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