Mindket

Qwen3.6-35B-A3B-8bit (MLX)

Newversion 1 · updated

Overview

8-bit MLX conversion of Qwen3.6-35B-A3B that reads text and images and answers in text, run with the mlx-vlm tool. For developers who want to run a large vision-language model locally.

Qwen3.6-35B-A3B-8bit (MLX) 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
  • The mlx-vlm package
  • Plenty of free disk space
Runs in
  • The mlx-vlm tool in a terminal
  • Python scripts that use mlx-vlm
  • Windows, macOS or Linux, for downloading the files
Start with
pip install -U mlx-vlm

Platform

WindowsOperating system
macOSOperating system
LinuxOperating system

About

What it is

This is an 8-bit copy of Qwen3.6-35B-A3B in MLX format, which reads both text and images and answers in text. It was converted with mlx-vlm version 0.4.4. The model has 35.1 billion parameters. For details about the model itself, the card sends you to the original Qwen3.6-35B-A3B card.

Who it suits

It suits developers and tinkerers who want to run a large vision-language model through the MLX toolchain on their own computer.

IndustryWho uses itHow they use itExample
SoftwareApp developersAdd image description to a local appDescribing screenshots from a script
ResearchModel testersTry an 8-bit conversion of a large modelComparing replies with another quantization
AccessibilityTool makersGenerate text descriptions of picturesWriting alt text for a photo

You can skip it if you do not use MLX, or if you want the original full-precision model.

Where to use it

It runs with the mlx-vlm command line tool and Python, on computers that support MLX, which is usually Apple hardware.

  • The mlx-vlm tool in a terminal
  • Python scripts that use mlx-vlm
  • Windows, macOS or Linux, for downloading the files
Use caseWhoWhat they get back
Image descriptionDevelopersA text description of a picture you pass in
Visual question answeringResearchersWritten answers about an image
Local chat with imagesHobbyistsReplies produced entirely on your computer

How to set it up

You need Python and the mlx-vlm package. Hugging Face lists the files at about 140 GB, so check free disk space first. The model downloads by its name the first time you run the command.

  • Python
  • The mlx-vlm package
  • Plenty of free disk space
  1. Install mlx-vlm with pip.
  2. Run the generate command with the model name and an image path.
pip install -U mlx-vlm

How to use it

Run the generate module with the model name, a token limit, a temperature, a prompt and the path to an image. Setting the temperature to 0.0 gives the same reply each time.

python -m mlx_vlm.generate --model mlx-community/Qwen3.6-35B-A3B-8bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
ToolWhat it does
mlx_vlm.generateRuns the model on a prompt and an image
--max-tokensLimits the length of the reply
--temperatureControls how random the reply is, 0.0 for steady output
--imageGives the path to the picture to describe

Example

You run the command with the prompt "Describe this image." and the path to a photo. The model prints a short text description of what the picture shows, up to 100 tokens long.

For newcomers and experts

If you are new, install mlx-vlm, pick a photo and run the one command above. Read the printed description to see what the model noticed.

If you know the field, this is a quantized conversion for MLX of a mixture-of-experts model from the Qwen3.6 series. The card adds no benchmark data of its own, so refer to the original model's card for capabilities and limits.

  1. Photo and a text prompt
  2. mlx-vlm loads the 8-bit model
  3. Model reads the image and prompt
  4. Text reply printed in the terminal

Good to know

The card is short and relies on the original Qwen3.6-35B-A3B card for everything else.

  • License: Apache 2.0
  • Cost to run: no API key, account or paid service is named, but the files are large
  • Data it reaches: the image and prompt you give it on your machine, plus files downloaded from Hugging Face
  • Not for: setups without MLX support

README and files

README

Qwen3.6-35B-A3B-8bit (MLX)

8-bit MLX conversion of Qwen3.6-35B-A3B that reads text and images and answers in text, run with the mlx-vlm tool. For developers who want to run a large vision-language model locally.

Made by mlx-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 copy of Qwen3.6-35B-A3B in MLX format, which reads both text and images and answers in text. It was converted with mlx-vlm version 0.4.4. The model has 35.1 billion parameters. For details about the model itself, the card sends you to the original Qwen3.6-35B-A3B card.

Who it suits: It suits developers and tinkerers who want to run a large vision-language model through the MLX toolchain on their own computer.

Industry | Who uses it | How they use it | Example
Software | App developers | Add image description to a local app | Describing screenshots from a script
Research | Model testers | Try an 8-bit conversion of a large model | Comparing replies with another quantization
Accessibility | Tool makers | Generate text descriptions of pictures | Writing alt text for a photo

You can skip it if you do not use MLX, or if you want the original full-precision model.

Where to use it: It runs with the mlx-vlm command line tool and Python, on computers that support MLX, which is usually Apple hardware.

  • The mlx-vlm tool in a terminal
  • Python scripts that use mlx-vlm
  • Windows, macOS or Linux, for downloading the files
Use case | Who | What they get back
Image description | Developers | A text description of a picture you pass in
Visual question answering | Researchers | Written answers about an image
Local chat with images | Hobbyists | Replies produced entirely on your computer

How to set it up: You need Python and the mlx-vlm package. Hugging Face lists the files at about 140 GB, so check free disk space first. The model downloads by its name the first time you run the command.

  • Python
  • The mlx-vlm package
  • Plenty of free disk space
  1. Install mlx-vlm with pip.
  2. Run the generate command with the model name and an image path.
pip install -U mlx-vlm

How to use it: Run the generate module with the model name, a token limit, a temperature, a prompt and the path to an image. Setting the temperature to 0.0 gives the same reply each time.

python -m mlx_vlm.generate --model mlx-community/Qwen3.6-35B-A3B-8bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
Tool | What it does
mlx_vlm.generate | Runs the model on a prompt and an image
--max-tokens | Limits the length of the reply
--temperature | Controls how random the reply is, 0.0 for steady output
--image | Gives the path to the picture to describe

Example: You run the command with the prompt "Describe this image." and the path to a photo. The model prints a short text description of what the picture shows, up to 100 tokens long.

For newcomers and experts: If you are new, install mlx-vlm, pick a photo and run the one command above. Read the printed description to see what the model noticed.

If you know the field, this is a quantized conversion for MLX of a mixture-of-experts model from the Qwen3.6 series. The card adds no benchmark data of its own, so refer to the original model's card for capabilities and limits.

Photo and a text prompt
mlx-vlm loads the 8-bit model
Model reads the image and prompt
Text reply printed in the terminal

Good to know: The card is short and relies on the original Qwen3.6-35B-A3B card for everything else.

  • License: Apache 2.0
  • Cost to run: no API key, account or paid service is named, but the files are large
  • Data it reaches: the image and prompt you give it on your machine, plus files downloaded from Hugging Face
  • Not for: setups without MLX support

On Hugging Face, 13 November 2026

  • Task: image text to text
  • Library: mlx
  • Parameters: 35.1B
  • Weights: about 140 GB in U32
  • Formats: Safetensors
  • Downloads in the last 30 days: 5,687
  • Likes: 37
  • License: apache-2.0
  • Based on: Qwen/Qwen3.6-35B-A3B
  • Quantized versions: 23
  • Tags: safetensors, qwen3_5_moe, 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 mlx-community/Qwen3.6-35B-A3B-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
Qwen3.6-35B-A3B-8bit (MLX) This page5,6873735.1Bapache-2.0
Ornith-1.5-35B-A3B-NVFP4Shares qwen3_5_moe · 8-bit · image-text-to-text1,124,3377319.5Bmit
Qwen3.6-35B-A3B-NVFP4-FastShares qwen3_5_moe · 8-bit · image-text-to-text695,02013419.8Bapache-2.0
Ornith-1.5-122B-NVFP4Shares qwen3_5_moe · 8-bit · image-text-to-text279,6633664Bmit
Qwen3.6-35B-A3B-NVFP4Shares qwen3_5_moe · 8-bit · conversational4,104,34567818.7Bapache-2.0
Qwen3.6-35B-A3BShares qwen3_5_moe · image-text-to-text · conversational4,071,9523,11936Bapache-2.0
Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13.

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