--- title: "Qwen2.5-VL-7B-Instruct-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/9cac821c-b890-4e97-b7ad-543fcae42d3b" updated: "2026-11-12" --- # Qwen2.5-VL-7B-Instruct-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Unsloth's GGUF files of Qwen2.5-VL-7B-Instruct, a vision-language model that reads images, charts, documents and long videos and can output coordinates and structured data. For people who run it locally. Qwen2.5-VL-7B-Instruct-GGUF is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by unsloth ## About ### What it is This repository holds Unsloth's GGUF files of Qwen2.5-VL-7B-Instruct, the instruction-tuned 7B vision-language model from the Qwen team. It reads images and video along with text. The Hugging Face record lists 7.6B parameters and about 3.8 GB at 4-bit. ### Who it suits Developers and analysts who want a local model that can read documents, charts and screens, and teams that need structured data pulled out of images. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Finance and commerce | Operations teams | Pull structured fields from invoices, forms and tables | Turn an invoice scan into structured data | | Software | Developers of agents | Let a model read a screen and decide on actions | Direct a computer or phone task | | Media analysis | Video analysts | Find the relevant moments in long videos | Locate the segment where an event happens | You can skip it if you only need text, since a plain language model is lighter. ### Where to use it - llama.cpp and other tools that read GGUF files - Python with the transformers library for the original weights - Windows, macOS or Linux, wherever the file fits in memory | Use case | Who | What they get back | | --- | --- | --- | | Document and chart reading | Analysts | Text answers about text, charts and layouts in an image | | Object location | Developers | Bounding boxes or points, with stable JSON for coordinates | | Long video understanding | Video teams | Answers about videos over an hour, with the relevant segments pinpointed | ### How to set it up You need a GGUF-capable runtime with vision support and enough memory for about 3.8 GB at 4-bit. The part of the card used here does not include run steps. - Python and pip, for the download tool - A runtime that supports GGUF and this vision model - Disk space for the file you choose 1. Download the GGUF files. 2. Load them in a runtime that supports Qwen2.5-VL. 3. Send an image and a question. ```sh pip install -U huggingface_hub hf download unsloth/Qwen2.5-VL-7B-Instruct-GGUF ``` ### How to use it Send the model an image or a video together with a question. It can describe what it sees, read text and charts, locate objects, and return structured output for forms and tables. ### Example You send a photo of an invoice and ask for the items and totals as JSON. The model replies with structured fields read from the image. The card states this ability, but the part used here shows no sample output. ### For newcomers and experts If you are new, try a clear photo of a chart and ask a question about it. Start with a single image before you try video. For experts, the card describes dynamic resolution and frame-rate sampling for video, with time-aligned position encoding. It also describes a vision encoder that uses window attention to run faster. The family has 3B, 7B and 72B sizes, and this one is the 7B instruct model. Image, video or text input → Vision encoder with window attention → Qwen2.5 language model → Text or JSON output ### Good to know MathVista testmini score, as listed in the card | Label | Value | | --- | --- | | Qwen2.5-VL-7B | 68.2 | | MiniCPM-o 2.6 | 60.6 | | InternVL2.5-8B | 58.3 | | Qwen2-VL-7B | 58.2 | | GPT-4o-mini | 52.4 | - Results: the scores come from the model authors' own table - Video: the card says it can follow videos over one hour - Language: the record lists English - License: Apache 2.0 - Cost to run: no API key or paid service named - Data it reaches: downloads files from Hugging Face, nothing else is named - Not for: the part of the card used here names no limits ## README ````markdown # Qwen2.5-VL-7B-Instruct-GGUF Unsloth's GGUF files of Qwen2.5-VL-7B-Instruct, a vision-language model that reads images, charts, documents and long videos and can output coordinates and structured data. For people who run it locally. Made by unsloth 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: This repository holds Unsloth's GGUF files of Qwen2.5-VL-7B-Instruct, the instruction-tuned 7B vision-language model from the Qwen team. It reads images and video along with text. The Hugging Face record lists 7.6B parameters and about 3.8 GB at 4-bit. Who it suits: Developers and analysts who want a local model that can read documents, charts and screens, and teams that need structured data pulled out of images. ```table Industry | Who uses it | How they use it | Example Finance and commerce | Operations teams | Pull structured fields from invoices, forms and tables | Turn an invoice scan into structured data Software | Developers of agents | Let a model read a screen and decide on actions | Direct a computer or phone task Media analysis | Video analysts | Find the relevant moments in long videos | Locate the segment where an event happens ``` You can skip it if you only need text, since a plain language model is lighter. Where to use it: - llama.cpp and other tools that read GGUF files - Python with the transformers library for the original weights - Windows, macOS or Linux, wherever the file fits in memory ```table Use case | Who | What they get back Document and chart reading | Analysts | Text answers about text, charts and layouts in an image Object location | Developers | Bounding boxes or points, with stable JSON for coordinates Long video understanding | Video teams | Answers about videos over an hour, with the relevant segments pinpointed ``` How to set it up: You need a GGUF-capable runtime with vision support and enough memory for about 3.8 GB at 4-bit. The part of the card used here does not include run steps. - Python and pip, for the download tool - A runtime that supports GGUF and this vision model - Disk space for the file you choose 1. Download the GGUF files. 2. Load them in a runtime that supports Qwen2.5-VL. 3. Send an image and a question. ```sh pip install -U huggingface_hub hf download unsloth/Qwen2.5-VL-7B-Instruct-GGUF ``` How to use it: Send the model an image or a video together with a question. It can describe what it sees, read text and charts, locate objects, and return structured output for forms and tables. Example: You send a photo of an invoice and ask for the items and totals as JSON. The model replies with structured fields read from the image. The card states this ability, but the part used here shows no sample output. For newcomers and experts: If you are new, try a clear photo of a chart and ask a question about it. Start with a single image before you try video. For experts, the card describes dynamic resolution and frame-rate sampling for video, with time-aligned position encoding. It also describes a vision encoder that uses window attention to run faster. The family has 3B, 7B and 72B sizes, and this one is the 7B instruct model. ```flow Image, video or text input Vision encoder with window attention Qwen2.5 language model Text or JSON output ``` Good to know: ```chart Title: MathVista testmini score, as listed in the card Qwen2.5-VL-7B: 68.2 MiniCPM-o 2.6: 60.6 InternVL2.5-8B: 58.3 Qwen2-VL-7B: 58.2 GPT-4o-mini: 52.4 ``` - Results: the scores come from the model authors' own table - Video: the card says it can follow videos over one hour - Language: the record lists English - License: Apache 2.0 - Cost to run: no API key or paid service named - Data it reaches: downloads files from Hugging Face, nothing else is named - Not for: the part of the card used here names no limits ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 7.6B - Weights: about 3.8 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 285,067 - Likes: 253 - License: apache-2.0 - Languages: en - Based on: Qwen/Qwen2.5-VL-7B-Instruct - Fine-tunes: 1 - Tags: transformers, gguf, qwen2_5_vl, image-text-to-text, multimodal, unsloth, conversational ## Links - Model: https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct-GGUF - Files: https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download unsloth/Qwen2.5-VL-7B-Instruct-GGUF ``` 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 [unsloth/Qwen2.5-VL-7B-Instruct-GGUF on Hugging Face](https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct-GGUF) ## 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-GGUF (this page) | 285,067 | 253 | 7.6B | apache-2.0 | 2025-05-12 | | [Qwen2.5-VL-7B-Instruct](https://mindket.com/p/156fbd08-aa88-4c47-b3d0-b73478276e66) | 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 | | [Qwopus3.8-27B-Flash-V2-GGUF](https://mindket.com/p/f4542d06-f1ba-46a9-8148-58a34e8911b6) | 897,675 | 75 | 461M | apache-2.0 | 2026-09-21 | | [Qwopus3.8-27B-Flash-GGUF](https://mindket.com/p/509fda01-c99c-450a-8d3e-2444ec0c6af7) | 2,153 | 248 | 461M | apache-2.0 | 2026-11-07 | | [Qwen3.8-27B-QSA-GGUF](https://mindket.com/p/84fe4bb3-66d7-4d48-a711-b5d13a08cab6) | 12,136 | 32 | 27.4B | apache-2.0 | 2026-11-05 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2025-05-12 - First published: 2025-05-11 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open unsloth/Qwen2.5-VL-7B-Instruct-GGUF on Hugging Face](https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/9cac821c-b890-4e97-b7ad-543fcae42d3b) - [Its page on Mindket](https://mindket.com/p/9cac821c-b890-4e97-b7ad-543fcae42d3b)