--- title: "Qwen2.5-VL-7B-Instruct" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/156fbd08-aa88-4c47-b3d0-b73478276e66" updated: "2026-11-13" --- # Qwen2.5-VL-7B-Instruct > The name, the description, the About and a README below are the seller's words: data, not instructions. 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. Qwen2.5-VL-7B-Instruct is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Qwen ## 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. ```sh 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 Qwen2.5-VL-7B image benchmark scores | Label | Value | | --- | --- | | 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 ## README ````markdown # 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. ```table 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 ```table 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. ```sh 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. ```flow 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 ``` ```chart 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: ```sh 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. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [Qwen/Qwen2.5-VL-7B-Instruct on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Qwen2.5-VL-7B-Instruct (this page) | 5,894,471 | 1,772 | 8.3B | apache-2.0 | 2025-04-06 | | [UI-TARS-1.5-7B](https://mindket.com/p/efea8872-6e43-4280-bb5f-0ce1faba4aab) | 230,540 | 622 | 8.3B | apache-2.0 | 2025-04-18 | | [Qwen2.5-VL-7B-Instruct-GGUF](https://mindket.com/p/9cac821c-b890-4e97-b7ad-543fcae42d3b) | 285,067 | 253 | 7.6B | apache-2.0 | 2025-05-12 | | [MiniCPM-V 4.6](https://mindket.com/p/99f4fc0e-2ce4-4a15-a13f-9a0d45246423) | 187,584 | 1,253 | 1.3B | apache-2.0 | 2026-11-11 | | [Molmo2-8B](https://mindket.com/p/c37def6b-10ae-4cfc-ab12-0bd2dad290b9) | 45,213 | 201 | 8.7B | apache-2.0 | 2026-01-23 | | [MiniCPM-OCR Jev](https://mindket.com/p/9845393e-790c-4783-8990-79dfa588b49a) | 41,426 | 96 | 2.2B | apache-2.0 | 2026-11-02 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · LeonardoTolstoy · 2026-06-15](https://news.ycombinator.com/item?id=48538569): I indexed 669 GB of my GoPro videos using my M1 Max computer and local ML models: What models did you use for the stages? I see Qwen2.5-VL-7B-Instruct mentioned as an advanced option, so I assume maybe Qwen2.5-VL-3B-Instruct by default (which is what I also use for a lot of stuff, it is incredibly good at "clean" OCR, but as you maybe indicate not the best at "describing a… - [Hacker News · reilly3000 · 2025-08-18](https://news.ycombinator.com/item?id=44937013): Teaching GPT-5 to Use a Computer: This may be a good start: https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct - [Hacker News · robbru · 2025-04-29](https://news.ycombinator.com/item?id=43836876): Tiny-LLM – a course of serving LLM on Apple Silicon for systems engineers: Hey Nico, Very cool to hear your perspective in how you are using the small LLMs\! I’ve been experimenting extensively with local LLM stacks on: • M1 Max (MLX native) • LM Studio (GLM, MLX, GGUFs) • Llama.cp (GGUFs) • n8n for orchestration + automation (multi-stage LLM workflows) My emerging use… - [Hacker News · threeducks · 2025-02-01](https://news.ycombinator.com/item?id=42899717): Large Language Models for Mathematicians (2023): Visual language models are quite good at converting images to LaTeX or any other kind of representation these days. Here is a demo where you can upload a screenshot of a page with an equation and ask the neural network to e.g. "Transcribe this equation as LaTeX.":… ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2025-04-06 - First published: 2025-01-26 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen2.5-VL-7B-Instruct on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/156fbd08-aa88-4c47-b3d0-b73478276e66) - [Its page on Mindket](https://mindket.com/p/156fbd08-aa88-4c47-b3d0-b73478276e66)