Qwen2-VL

About

What Qwen2-VL is

Qwen2-VL-7B-Instruct-AWQ Introduction We're excited to unveil Qwen2-VL, the latest iteration of our Qwen-VL model, representing nearly a year of innovation. What’s New in Qwen2-VL? Key Enhancements: SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc. Understanding videos of 20min+: Qwen2-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc. Agent that can operate your mobiles, robots, etc.: with the abilities of complex reasoning and decision making, Qwen2-VL can be integrated with devices like mobile phones, robots, etc., for automatic operation based on visual environment and text instructions. Multilingual Support: to serve global users, besides English and Chinese, Qwen2-VL now supports the understanding of texts in different languages inside images, including most European languages, Japanese, Korean, Arabic, Vietnamese, etc. Model Architecture Updates: Naive Dynamic Resolution: Unlike before, Qwen2-VL can handle arbitrary image resolutions, mapping them into a dynamic number of visual tokens, offering a more human-like visual processing experience. Multimodal Rotary Position Embedding (M-ROPE): Decomposes positional embedding into parts to capture 1D textual, 2D visual, and 3D video positional information, enhancing its multimodal processing capabilities. We have three models with 2, 7 and 72 billion parameters. This repo contains the instruction-tuned 7B Qwen2-VL model. For more information, visit our Blog and GitHub. Benchmark Performance of Quantized Models This section reports the generation performance of quantized models (including GPTQ and AWQ) of the Qwen2-VL series. Specifically, we report: - MMMUVAL (Accuracy) - DocVQAVAL (Accuracy) - MMBenchDEVEN (Accuracy) - MathVistaMINI (Accuracy) We use VLMEvalkit to evaluate all models. Speed Benchmark This section reports the speed performance of bf16 models, quantized models (including GPTQ-Int4, GPTQ-Int8 and AWQ) of the Qwen2-VL series. Specifically, we report the inference speed (tokens/s) as w

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys qwen2-vl for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (qwen2-vl below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen2-vl","messages":[{"role":"user","content":"Hello"}]}'

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

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