Qwen2.5-VL-7B-Instruct

Qwen2.5-VL-7B-Instruct

A vision-language model that reads images, documents and video together with a text question and answers in text. Suits developers who build apps around charts, scans, screens and clips.

Made by Qwen on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

About

What it is: Qwen2.5-VL-7B-Instruct is a vision-language model from Qwen that reads images and video together with text. You show it a picture, a document or a clip and ask a question, and it answers in text. It is the instruction-tuned 7B member of a family with 3, 7 and 72 billion parameter sizes.

Who it suits: Developers building apps that need to read documents, charts and screens, and teams who want an open multimodal model they can run themselves. Researchers who work on video or visual agents will also find it useful.

Industry | Who uses it | How they use it | Example
Finance | Back-office teams | Pull fields out of scanned invoices and forms | Turn an invoice scan into structured data
Commerce | Product and data teams | Read tables, charts and layouts in images | Extract a table from a screenshot
Software | App developers | Add image and video questions to their apps | Ask what a chart shows
Research | Vision and agent researchers | Test grounding, long video and screen control | Mark objects with boxes or points

Anyone who only needs a text chat model can skip it, since the vision parts add weight and setup.

Where to use it:

• Python scripts that use the transformers library
• Windows, macOS or Linux, after you download the files with Hugging Face's hub tool
• Apps that send images, scanned pages or video clips together with a text question

Use case | Who | What they get back
Read documents and charts | Data and finance teams | Text and structured output from scans, forms and tables
Locate objects in a picture | Vision developers | Bounding boxes or points in stable JSON
Follow long video | Media and analytics teams | Answers about videos over an hour, with the relevant segment pinpointed
Drive a screen | Agent builders | Reasoning and tool directions for computer or phone use

How to set it up:

• Python with pip
• Memory for the weights, about 16.6 GB in BF16 or about 4.1 GB at 4-bit (an estimate)
• The transformers library, which this model is built for

1. Install the Hugging Face hub package.
2. Download the model files to your computer.
3. Open the model card on Hugging Face for the loading code, then load the model with transformers.

pip install -U huggingface_hub
hf download Qwen/Qwen2.5-VL-7B-Instruct

How to use it: Load the model in a Python script, hand it an image or a video together with a written question, and read the text that comes back. The card on Hugging Face shows the exact loading code, which is not repeated here.

Example: You give it a scan of an invoice and ask it to return the contents as structured fields. It replies with the invoice details laid out as data you can feed into other tools.

For newcomers and experts: If you are new, start with one image and one question, such as asking what a chart shows. Check the answer against the picture before you try video or long documents.

Experts will notice the vision encoder uses window attention in the ViT, with SwiGLU and RMSNorm to match the Qwen2.5 language model. Video training uses dynamic resolution and dynamic frame rate sampling, and mRoPE is aligned to absolute time. That alignment lets it locate specific moments. The model has about 8.3B parameters in total.

You send an image or video and a question
The vision encoder reads the visuals
The language model combines them with your text
It replies with text, JSON or coordinates

Title: Qwen2.5-VL-7B image benchmark scores
DocVQA test: 95.7 score
ChartQA test: 87.3 score
TextVQA val: 84.9 score
InfoVQA test: 82.6 score
MMBench-V1.1-En test: 82.6 score
MathVista testmini: 68.2 score

Good to know:

• Agent tests: the card lists 29.0 on ScreenSpot Pro and 25.5 on AndroidWorld_SR, so hard screen tasks are not solved
• License: Apache 2.0
• Cost to run: none named, no API key or account, only hardware with enough memory
• Data it reaches: it downloads files from Hugging Face, and reads whatever images and videos you give it
• Not for: the source names no forbidden uses, and it is built for images, video and text, not audio

On Hugging Face, 13 November 2026

• Task: image text to text
• Library: transformers
• Parameters: 8.3B
• Weights: about 16.6 GB in BF16; about 4.1 GB at 4-bit (estimate)
• Formats: Safetensors
• Downloads in the last 30 days: 5,894,471
• Likes: 1,772
• License: apache-2.0
• Languages: en
• Quantized versions: 160
• Fine-tunes: 1,269
• Tags: transformers, safetensors, qwen2_5_vl, image-text-to-text, multimodal, conversational

Links

• Model: https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct
• Files: https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download Qwen/Qwen2.5-VL-7B-Instruct

Then follow the usage in its model card. Nothing in this kit runs by itself.