Mindket

Qwen3.8-27B-MLX-8bit

Newversion 1 · updated

Overview

Runs a 27B model that reads images and text in an 8-bit MLX build for Apple Silicon Macs. It suits people who want picture questions answered locally.

Qwen3.8-27B-MLX-8bit is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on . Download it on this page, a zip of its files. Nothing in it runs by itself.

At a glance

You need
  • Python on your computer, so you can run pip
  • An Apple Silicon Mac to run the model
  • Room for about 109 GB of weights on disk
Runs in
  • Apple Silicon Macs, the hardware this copy is tuned for
  • Python tools built on MLX, such as the mlx_vlm library that made this copy
  • Windows, macOS or Linux with Python, for downloading the files only
Start with
pip install -U huggingface_hub
hf download lmstudio-community/Qwen3.8-27B-MLX-8bit

Platform

WindowsOperating system
macOSOperating system
LinuxOperating system

About

What it is

This is an 8-bit MLX copy of the Qwen3.8-27B model, prepared by the LM Studio team for Apple Silicon Macs. The model takes an image plus a text prompt and answers in text. It has 27.4B parameters.

Who it suits

It suits people with an Apple Silicon Mac who want a large model that reads pictures and writes text on their own machine.

IndustryWho uses itHow they use itExample
SoftwareApp developersRun the model locally to answer questions about screenshotsA developer feeds in a screen capture and asks which buttons are visible
ResearchMachine learning researchersCompare this 8-bit copy with the original modelA researcher asks both the same image question and reads the two answers
EducationStudents and hobbyistsTry a large image and text model on a personal MacA student asks for a description of a diagram

People without an Apple Silicon Mac, or who prefer a hosted service, can skip it.

Where to use it

  • Apple Silicon Macs, the hardware this copy is tuned for
  • Python tools built on MLX, such as the mlx_vlm library that made this copy
  • Windows, macOS or Linux with Python, for downloading the files only
Use caseWhoWhat they get back
Describe a pictureDeveloperA short text description of the image
Answer a question about a screenshotAnalystA text answer based on what the screenshot shows
Compare it with the original modelResearcherTwo sets of answers to read side by side

How to set it up

  • Python on your computer, so you can run pip
  • An Apple Silicon Mac to run the model
  • Room for about 109 GB of weights on disk
  1. Install the Hugging Face hub package.
  2. Download the model files to your computer.
  3. Open the downloaded files in an MLX tool for image and text models. The card names mlx_vlm as the library used to make this copy.
pip install -U huggingface_hub
hf download lmstudio-community/Qwen3.8-27B-MLX-8bit

How to use it

Once the files are on your Mac, you give the model an image and a text question, and it sends back text. The only commands the source shows are the download steps.

hf download lmstudio-community/Qwen3.8-27B-MLX-8bit
ToolWhat it does
hf downloadFetches the model files from Hugging Face to your computer
pip install -U huggingface_hubInstalls or upgrades the package that provides the hf command
mlx_vlmThe MLX library the 8-bit copy was made with

Example

Say you attach a photo of a whiteboard and ask, "Which tasks on this board have no owner?" The model reads the picture and replies in plain text with the tasks it finds.

For newcomers and experts

A model is a large file that turns an input into an answer. This one takes a picture and some words and writes words back. A small first step is to run the two download commands and look at what lands in the folder.

For experts, this is Qwen3.8-27B with 27.4B parameters, stored in 8 bit for Apple's MLX framework and made with mlx_vlm. The files use the Safetensors format. Lower precision can cost some accuracy against the original, and the copy is tuned for Apple Silicon rather than other hardware.

  1. Qwen publishes Qwen3.8-27B
  2. LM Studio team makes the 8-bit MLX copy
  3. You download the files with hf
  4. An MLX tool on a Mac loads them
  5. You send an image and text, it writes text back

Good to know

  • Size: about 109 GB of weights and 27.4B parameters
  • License: Apache 2.0, as Hugging Face lists it
  • Cost to run: none named, though the weights need a lot of disk space
  • Data it reaches: downloading uses the web and writes files to your disk, and the source names no other access
  • Not for: trusting answers unchecked, since the LM Studio disclaimer says community models can give offensive, harmful, inaccurate or deceptive output with no warranty

README and files

README

Qwen3.8-27B-MLX-8bit

Runs a 27B model that reads images and text in an 8-bit MLX build for Apple Silicon Macs. It suits people who want picture questions answered locally.

Made by lmstudio-community 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 is an 8-bit MLX copy of the Qwen3.8-27B model, prepared by the LM Studio team for Apple Silicon Macs. The model takes an image plus a text prompt and answers in text. It has 27.4B parameters.

Who it suits: It suits people with an Apple Silicon Mac who want a large model that reads pictures and writes text on their own machine.

Industry | Who uses it | How they use it | Example
Software | App developers | Run the model locally to answer questions about screenshots | A developer feeds in a screen capture and asks which buttons are visible
Research | Machine learning researchers | Compare this 8-bit copy with the original model | A researcher asks both the same image question and reads the two answers
Education | Students and hobbyists | Try a large image and text model on a personal Mac | A student asks for a description of a diagram

People without an Apple Silicon Mac, or who prefer a hosted service, can skip it.

Where to use it:

  • Apple Silicon Macs, the hardware this copy is tuned for
  • Python tools built on MLX, such as the mlx_vlm library that made this copy
  • Windows, macOS or Linux with Python, for downloading the files only
Use case | Who | What they get back
Describe a picture | Developer | A short text description of the image
Answer a question about a screenshot | Analyst | A text answer based on what the screenshot shows
Compare it with the original model | Researcher | Two sets of answers to read side by side

How to set it up:

  • Python on your computer, so you can run pip
  • An Apple Silicon Mac to run the model
  • Room for about 109 GB of weights on disk
  1. Install the Hugging Face hub package.
  2. Download the model files to your computer.
  3. Open the downloaded files in an MLX tool for image and text models. The card names mlx_vlm as the library used to make this copy.
pip install -U huggingface_hub
hf download lmstudio-community/Qwen3.8-27B-MLX-8bit

How to use it: Once the files are on your Mac, you give the model an image and a text question, and it sends back text. The only commands the source shows are the download steps.

hf download lmstudio-community/Qwen3.8-27B-MLX-8bit
Tool | What it does
hf download | Fetches the model files from Hugging Face to your computer
pip install -U huggingface_hub | Installs or upgrades the package that provides the hf command
mlx_vlm | The MLX library the 8-bit copy was made with

Example: Say you attach a photo of a whiteboard and ask, "Which tasks on this board have no owner?" The model reads the picture and replies in plain text with the tasks it finds.

For newcomers and experts: A model is a large file that turns an input into an answer. This one takes a picture and some words and writes words back. A small first step is to run the two download commands and look at what lands in the folder.

For experts, this is Qwen3.8-27B with 27.4B parameters, stored in 8 bit for Apple's MLX framework and made with mlx_vlm. The files use the Safetensors format. Lower precision can cost some accuracy against the original, and the copy is tuned for Apple Silicon rather than other hardware.

Qwen publishes Qwen3.8-27B
LM Studio team makes the 8-bit MLX copy
You download the files with hf
An MLX tool on a Mac loads them
You send an image and text, it writes text back

Good to know:

  • Size: about 109 GB of weights and 27.4B parameters
  • License: Apache 2.0, as Hugging Face lists it
  • Cost to run: none named, though the weights need a lot of disk space
  • Data it reaches: downloading uses the web and writes files to your disk, and the source names no other access
  • Not for: trusting answers unchecked, since the LM Studio disclaimer says community models can give offensive, harmful, inaccurate or deceptive output with no warranty

On Hugging Face, 13 November 2026

  • Task: image text to text
  • Library: transformers
  • Parameters: 27.4B
  • Weights: about 109 GB in U32
  • Formats: Safetensors
  • Downloads in the last 30 days: 2,683,350
  • Likes: 56
  • License: apache-2.0
  • Based on: Qwen/Qwen3.8-27B
  • Tags: transformers, safetensors, qwen3_5, image-text-to-text, conversational, 8-bit

Links

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download lmstudio-community/Qwen3.8-27B-MLX-8bit

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

Files
README.md  5 KB

1 file, 5 KB, downloaded as a zip.

What’s new

  • Version 1 · 2026-11-13

Compare with similar

Listed Hugging Face models in the same category that share the most tags with this one.

ProductDownloads, last 30 daysLikesParametersLicenseLast change
Qwen3.8-27B-MLX-8bit This page2,683,3505627.4Bapache-2.0
Qwen3.6-35B-A3B-NVFP4-FastShares 8-bit · qwen3_5 · image-text-to-text695,02013419.8Bapache-2.0
Qwen3.8-27B-Quark-AWQ-MXFP4Shares 8-bit · qwen3_5 · image-text-to-text40,2584415.6Bapache-2.0
Qwen3.8-27B-NVFP4 by RadixArkShares 8-bit · qwen3_5 · image-text-to-text979,44512618.2Bapache-2.0
Qwen3.8-27B-oQ8e-mtpShares 8-bit · qwen3_5 · image-text-to-text2,9541627.8Bapache-2.0
DeepSeek-V4.1-FlashShares 8-bit · image-text-to-text · conversational1,759,3354,949763Bmit
Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13.

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