Qwen-VL

GPT-4V、Claude 3、Gemini是现代多模态大模型的代表，能够同时处理文本、图像等多种数据类型

Qwen-VL is a vision-language model series launched by Alibaba DAMO Academy, extended from the Qwen large language model, adopting a combined architecture of a ViT visual encoder and the Qwen language model, and achieving cross-modal alignment through a position-aware visual-language adapter.

VLMs (Vision Language Models) typically consist of a visual encoder (such as ViT), a language model, and a projection layer connecting the two, with representative models including GPT-4V/4o, Claude 3.5, the LLaVA series, and InternVL

The Qwen Vision model (Qwen-VL series) is a multimodal large model launched by Alibaba Cloud's Tongyi team, capable of simultaneously understanding text and image inputs.

The Qwen-VL series models adopt a dynamic resolution input mechanism, enabling them to process images of different sizes without forced scaling.

Open-source multimodal models LLaVA, InternVL, and Qwen-VL typically adopt a 'vision encoder + projection layer + language model' architecture.

Text models supported by the Transformers framework include GPT, BERT, LLaMA, and T5; vision models include ViT, CLIP, and Stable Diffusion; audio models include Whisper and Wav2Vec; and multimodal models include LLaVA and Qwen-VL.

Qwen-VL和InternVL分别来自阿里和上海AI Lab，代表国内多模态领域的前沿水平

阿里的Qwen-VL和智谱的CogVLM/GLM-4V是中文领域代表性的多模态模型

开源多模态模型包括LLaVA、MiniCPM-V、InternVL、Qwen-VL等

The OCR route commonly uses tools such as Tesseract and PaddleOCR to first obtain plain text and then hand it to a language model to determine the fields; end-to-end vision models (such as Donut and Qwen-VL) skip explicit OCR and predict structured output directly from the image.

Currently mainstream open-weight vision/text models include Meta's Llama series, Mistral, Qwen-VL, LLaVA, and Donut and LayoutLMv3, which specialize in document understanding; their weights can be loaded through runtimes such as llama.cpp, Ollama, and Transformers, and they work offline.

Multimodal large language models (MLLMs) refer to large-scale models capable of processing multiple modal inputs such as text, images, and video simultaneously, such as GPT-4V and Qwen-VL.

Multimodal large language models (MLLMs) refer to large-scale models capable of simultaneously processing multiple modality inputs such as text, images, audio, and video and generating text output, with representative works including GPT-4o, Gemini, Qwen-VL, etc.

Qwen系列的多分支包括针对代码场景继续预训练的CodeQwen以及融合视觉编码器的多模态版本Qwen-VL

The specific benchmark results of MiMo-V2.6 and its horizontal comparison with similar open-source omnimodal models (such as the Qwen-VL series) remain to be tested.

Alibaba previously released the Qwen-Audio and Qwen-VL series of models.

A VLM is a multimodal model that integrates a visual encoder on top of a traditional LLM, with typical representatives including LLaVA, Qwen-VL, and InternVL.

The context length of mainstream open-source multimodal models is typically between 4K and 32K tokens: LLaVA-1.5 is 4,096 tokens, Qwen-VL is 8,192 tokens, and InternVL2 extends to 32K.

Mainstream open-source models focused on document understanding, such as InternVL2 and Qwen-VL, mostly have parameter counts in the range of 3B to 72B.