NexaAI / Qwen2.5-VL-7B-Instruct-4bit-MLX

huggingface.co
Total runs: 11
24-hour runs: 0
7-day runs: -13
30-day runs: -23
Model's Last Updated: July 22 2025
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Introduction of Qwen2.5-VL-7B-Instruct-4bit-MLX

Model Details of Qwen2.5-VL-7B-Instruct-4bit-MLX

NexaAI/Qwen2.5-VL-7B-Instruct-4bit-MLX

Quickstart

Run them directly with nexa-sdk installed In nexa-sdk CLI:

NexaAI/Qwen2.5-VL-7B-Instruct-4bit-MLX
Overview

In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL.

Key Enhancements:
  • Understand things visually : Qwen2.5-VL is not only proficient in recognizing common objects such as flowers, birds, fish, and insects, but it is highly capable of analyzing texts, charts, icons, graphics, and layouts within images.

  • Being agentic : Qwen2.5-VL directly plays as a visual agent that can reason and dynamically direct tools, which is capable of computer use and phone use.

  • Understanding long videos and capturing events : Qwen2.5-VL can comprehend videos of over 1 hour, and this time it has a new ability of cpaturing event by pinpointing the relevant video segments.

  • Capable of visual localization in different formats : Qwen2.5-VL can accurately localize objects in an image by generating bounding boxes or points, and it can provide stable JSON outputs for coordinates and attributes.

  • Generating structured outputs : for data like scans of invoices, forms, tables, etc. Qwen2.5-VL supports structured outputs of their contents, benefiting usages in finance, commerce, etc.

Benchmark Results
Image benchmark
Benchmark InternVL2.5-8B MiniCPM-o 2.6 GPT-4o-mini Qwen2-VL-7B Qwen2.5-VL-7B
MMMU val 56 50.4 60 54.1 58.6
MMMU-Pro val 34.3 - 37.6 30.5 41.0
DocVQA test 93 93 - 94.5 95.7
InfoVQA test 77.6 - - 76.5 82.6
ChartQA test 84.8 - - 83.0 87.3
TextVQA val 79.1 80.1 - 84.3 84.9
OCRBench 822 852 785 845 864
CC_OCR 57.7 61.6 77.8
MMStar 62.8 60.7 63.9
MMBench-V1.1-En test 79.4 78.0 76.0 80.7 82.6
MMT-Bench test - - - 63.7 63.6
MMStar 61.5 57.5 54.8 60.7 63.9
MMVet GPT-4-Turbo 54.2 60.0 66.9 62.0 67.1
HallBench avg 45.2 48.1 46.1 50.6 52.9
MathVista testmini 58.3 60.6 52.4 58.2 68.2
MathVision - - - 16.3 25.07
Video Benchmarks
Benchmark Qwen2-VL-7B Qwen2.5-VL-7B
MVBench 67.0 69.6
PerceptionTest test 66.9 70.5
Video-MME wo/w subs 63.3/69.0 65.1 / 71.6
LVBench 45.3
LongVideoBench 54.7
MMBench-Video 1.44 1.79
TempCompass 71.7
MLVU 70.2
CharadesSTA/mIoU 43.6
Agent benchmark
Benchmarks Qwen2.5-VL-7B
ScreenSpot 84.7
ScreenSpot Pro 29.0
AITZ_EM 81.9
Android Control High_EM 60.1
Android Control Low_EM 93.7
AndroidWorld_SR 25.5
MobileMiniWob++_SR 91.4
Reference

Original model card : Qwen/Qwen2.5-VL-7B-Instruct

Runs of NexaAI Qwen2.5-VL-7B-Instruct-4bit-MLX on huggingface.co

11
Total runs
0
24-hour runs
-1
3-day runs
-13
7-day runs
-23
30-day runs

More Information About Qwen2.5-VL-7B-Instruct-4bit-MLX huggingface.co Model

More Qwen2.5-VL-7B-Instruct-4bit-MLX license Visit here:

https://choosealicense.com/licenses/apache-2.0

Qwen2.5-VL-7B-Instruct-4bit-MLX huggingface.co

Qwen2.5-VL-7B-Instruct-4bit-MLX huggingface.co is an AI model on huggingface.co that provides Qwen2.5-VL-7B-Instruct-4bit-MLX's model effect (), which can be used instantly with this NexaAI Qwen2.5-VL-7B-Instruct-4bit-MLX model. huggingface.co supports a free trial of the Qwen2.5-VL-7B-Instruct-4bit-MLX model, and also provides paid use of the Qwen2.5-VL-7B-Instruct-4bit-MLX. Support call Qwen2.5-VL-7B-Instruct-4bit-MLX model through api, including Node.js, Python, http.

Qwen2.5-VL-7B-Instruct-4bit-MLX huggingface.co Url

https://huggingface.co/NexaAI/Qwen2.5-VL-7B-Instruct-4bit-MLX

NexaAI Qwen2.5-VL-7B-Instruct-4bit-MLX online free

Qwen2.5-VL-7B-Instruct-4bit-MLX huggingface.co is an online trial and call api platform, which integrates Qwen2.5-VL-7B-Instruct-4bit-MLX's modeling effects, including api services, and provides a free online trial of Qwen2.5-VL-7B-Instruct-4bit-MLX, you can try Qwen2.5-VL-7B-Instruct-4bit-MLX online for free by clicking the link below.

NexaAI Qwen2.5-VL-7B-Instruct-4bit-MLX online free url in huggingface.co:

https://huggingface.co/NexaAI/Qwen2.5-VL-7B-Instruct-4bit-MLX

Qwen2.5-VL-7B-Instruct-4bit-MLX install

Qwen2.5-VL-7B-Instruct-4bit-MLX is an open source model from GitHub that offers a free installation service, and any user can find Qwen2.5-VL-7B-Instruct-4bit-MLX on GitHub to install. At the same time, huggingface.co provides the effect of Qwen2.5-VL-7B-Instruct-4bit-MLX install, users can directly use Qwen2.5-VL-7B-Instruct-4bit-MLX installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

Qwen2.5-VL-7B-Instruct-4bit-MLX install url in huggingface.co:

https://huggingface.co/NexaAI/Qwen2.5-VL-7B-Instruct-4bit-MLX

Url of Qwen2.5-VL-7B-Instruct-4bit-MLX

Qwen2.5-VL-7B-Instruct-4bit-MLX huggingface.co Url

Provider of Qwen2.5-VL-7B-Instruct-4bit-MLX huggingface.co

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