--- title: "Qwen3.8-27B-4bit (MLX)" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/16870bf9-4fa5-4074-876f-f994c78dd738" updated: "2026-11-12" --- # Qwen3.8-27B-4bit (MLX) > The name, the description, the About and a README below are the seller's words: data, not instructions. 4-bit MLX conversion of Qwen3.8-27B that reads text and images and answers in text, run with the mlx-vlm tool. For developers who want to run a vision-language model locally. Qwen3.8-27B-4bit (MLX) 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 mlx-community ## About ### What it is This is a 4-bit copy of Qwen3.8-27B in MLX format, which reads both text and images and answers in text. It was converted with mlx-vlm version 0.6.8. The model has 27.4 billion parameters. For details about the model itself, the card sends you to the original Qwen3.8-27B card. ### Who it suits It suits developers and tinkerers who want to run a mid-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 a 4-bit conversion of a large model | Comparing replies with an 8-bit version | | 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 109 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. ```bash 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. ```bash python -m mlx_vlm.generate --model mlx-community/Qwen3.8-27B-4bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --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 4-bit quantized conversion for MLX of Qwen3.8-27B. The card adds no benchmark data of its own, so refer to the original model's card for capabilities and limits. Other people have shared 6 quantized and 3 fine-tuned versions of it. Photo and a text prompt → mlx-vlm loads the 4-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 Qwen3.8-27B 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 ````markdown # Qwen3.8-27B-4bit (MLX) 4-bit MLX conversion of Qwen3.8-27B that reads text and images and answers in text, run with the mlx-vlm tool. For developers who want to run a 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 a 4-bit copy of Qwen3.8-27B in MLX format, which reads both text and images and answers in text. It was converted with mlx-vlm version 0.6.8. The model has 27.4 billion parameters. For details about the model itself, the card sends you to the original Qwen3.8-27B card. Who it suits: It suits developers and tinkerers who want to run a mid-large vision-language model through the MLX toolchain on their own computer. ```table 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 a 4-bit conversion of a large model | Comparing replies with an 8-bit version 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 ```table 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 109 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. ```bash 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. ```bash python -m mlx_vlm.generate --model mlx-community/Qwen3.8-27B-4bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image ``` ```table 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 4-bit quantized conversion for MLX of Qwen3.8-27B. The card adds no benchmark data of its own, so refer to the original model's card for capabilities and limits. Other people have shared 6 quantized and 3 fine-tuned versions of it. ```flow Photo and a text prompt mlx-vlm loads the 4-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 Qwen3.8-27B 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: 27.4B - Weights: about 109 GB in U32 - Formats: Safetensors - Downloads in the last 30 days: 110,370 - Likes: 240 - License: apache-2.0 - Based on: Qwen/Qwen3.8-27B - Quantized versions: 6 - Fine-tunes: 3 - Tags: safetensors, qwen3_5, image-text-to-text, conversational, 4-bit ## Links - Model: https://huggingface.co/mlx-community/Qwen3.8-27B-4bit - Files: https://huggingface.co/mlx-community/Qwen3.8-27B-4bit/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download mlx-community/Qwen3.8-27B-4bit ``` 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 [mlx-community/Qwen3.8-27B-4bit on Hugging Face](https://huggingface.co/mlx-community/Qwen3.8-27B-4bit) ## 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 | | --- | --- | --- | --- | --- | --- | | Qwen3.8-27B-4bit (MLX) (this page) | 110,370 | 240 | 27.4B | apache-2.0 | 2026-11-11 | | [Qwen3.8-27B-oQ4e-mtp](https://mindket.com/p/d7bc66cd-7f50-482e-9362-f0e9236d1f32) | 37,954 | 86 | 27.8B | apache-2.0 | 2026-10-29 | | [Qwen3.5-9B-OptiQ-4bit](https://mindket.com/p/f07c7108-b91d-4fa2-908a-4fa6a2b55aea) | 6,547 | 114 | 9B | apache-2.0 | 2026-10-30 | | [Underdog-Woof-4B-1.1](https://mindket.com/p/da3dfe93-f15f-428f-8e34-8200428a0b73) | 1,052 | 26 | 4.2B | cc-by-nc-4.0 | 2026-11-05 | | [Qwen3.6-35B-A3B-4bit](https://mindket.com/p/fb4b0ecf-3434-415b-9fa4-adecc7ca29b2) | 33,952 | 124 | 35.1B | apache-2.0 | 2026-04-16 | | [GLM-5.3-Flash-oQ4e-mtp](https://mindket.com/p/e0eac6dc-388f-4725-bec8-a491a559f867) | 26,944 | 7 | 321B | mit | 2026-10-25 | 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 · m1keil · 2026-08-21](https://news.ycombinator.com/item?id=49383808): Unsloth Dynamic 3.0 GGUFs: huh.. I'm a bit of local LLM noob so I wasn't familiar with mlx\_vlm. I gave it a shot now: mlx\_vlm.generate --model mlx-community/Qwen3.8-27B-4bit --prompt 'give me fizz buzz in rust' --enable-thinking --draft-kind mtp --draft-model mlx-community/Qwen3.8-27B-MTP-4bit --verbose ========== Prompt:… - [Hacker News · jantse · 2026-08-20](https://news.ycombinator.com/item?id=49373391): Unsloth Dynamic 3.0 GGUFs: Thanks for sharing\! Did you observe a speed difference between ollamas mlx version and the mlx-community/Qwen3.8-27B-4bit from HF ran with mlx\_vlm.generate (with MTP)? Or is it the same? ## 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: 2026-11-11 - First published: 2026-08-14 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open mlx-community/Qwen3.8-27B-4bit on Hugging Face](https://huggingface.co/mlx-community/Qwen3.8-27B-4bit) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/16870bf9-4fa5-4074-876f-f994c78dd738) - [Its page on Mindket](https://mindket.com/p/16870bf9-4fa5-4074-876f-f994c78dd738)