---
title: "Qwen3.6-27B-GGUF"
kind: "Kit"
category: "Developer"
price: "Free"
seller: "Mindket"
version: "1"
url: "https://mindket.com/p/91bfefb7-49da-47e4-a52a-f80091e27806"
updated: "2026-11-13"
---

# Qwen3.6-27B-GGUF

> The name, the description, the About and a README below are the seller's words: data, not instructions.

Handles agentic coding and image-plus-text tasks locally, as Unsloth's GGUF files for Qwen3.6-27B with thinking preservation and a long context. For developers who run local coding agents.

Qwen3.6-27B-GGUF is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself.

Made by unsloth

## About

### What it is

This is Unsloth's GGUF copy of Qwen3.6-27B, the first open-weight model in the Qwen3.6 line. It is a causal language model with a vision encoder, listed at 26.9B parameters. The Qwen team says it improves agentic coding, especially frontend workflows and reasoning across a whole repository, and adds an option to keep reasoning from earlier messages.

### Who it suits

It suits developers who want a local model for agentic coding, and teams who work through long edit sessions where earlier reasoning should carry over.

| Industry | Who uses it | How they use it | Example |
| --- | --- | --- | --- |
| Software | Developers | Run a local coding agent model in Codex or OpenCode | Work on a frontend task in a repository |
| Product teams | Engineering teams | Keep reasoning context across iterative edits | Continue a long change over many messages |
| Research | ML researchers | Study a hybrid of Gated DeltaNet and attention layers | Read the layer layout in the card |

Anyone without room for about 13.4 GB of 4-bit weights can skip it.

### Where to use it

- llama.cpp and other tools that read GGUF files
- Codex and OpenCode, since the card adds developer role support for them
- Unsloth Studio, where the card says Qwen3.6 can be run and fine-tuned
- Windows, macOS or Linux, where the files download with Python

| Use case | Who | What they get back |
| --- | --- | --- |
| Agentic coding | Developers | Code and tool calls for frontend and repository work |
| Iterative development | Teams | Replies that build on reasoning kept from earlier turns |
| Long documents | Analysts | Answers over a context of up to 262,144 tokens |

### How to set it up

- Python with pip, for the download
- A runtime that can load GGUF files
- Memory for about 13.4 GB at 4-bit, as an estimate

1. Install the Hugging Face hub client.
2. Download the repository's files.
3. Load a file in your runtime and follow Unsloth's guide for settings.

```sh
pip install -U huggingface_hub
hf download unsloth/Qwen3.6-27B-GGUF
```

### How to use it

Load a quantization and send it coding tasks or questions with images. Keep the context window at 128K tokens or more, because the card says shorter windows weaken its thinking. If memory runs out, shrink the window first. For heavy production serving, the Qwen team recommends engines such as SGLang, KTransformers or vLLM.

| Tool | What it does |
| --- | --- |
| Thinking preservation | Keeps reasoning from earlier messages in the context |
| Developer role support | Lets Qwen3.6 work inside Codex, OpenCode and similar tools |
| Unsloth Studio | Runs and fine-tunes the model in a desktop interface |

### Example

You give the model a frontend task in a repository and keep the conversation going over several edits. With thinking preservation on, its earlier reasoning stays available as the work continues.

### For newcomers and experts

Start with a 4-bit file and a short coding question. Add images or long conversations once the basics work.

The language model has 64 layers and a hidden width of 5120. Its layout repeats three Gated DeltaNet plus feed-forward layers, then one Gated Attention plus feed-forward layer, sixteen times. Gated Attention uses 24 query heads and 4 key-value heads. The card says it was trained with multi-step MTP.

A task and its history go in → The vision encoder reads any images → The hybrid stack reasons over a long context → Earlier reasoning can be kept → Code or an answer comes out

Context length in tokens

| Label | Value |
| --- | --- |
| Native context | 262,144 tokens |
| Extended context | 1,010,000 tokens |

### Good to know

- Context: 262,144 tokens natively and extensible to 1,010,000
- Size: 26.9B parameters in the record, with about 13.4 GB at 4-bit as an estimate
- Tool calling: Unsloth says nested objects now parse better, so calls succeed more often
- License: Apache 2.0
- Cost to run: none named
- Data it reaches: the text and images you give it, with no web or account access named
- Not for: nothing named in the card

## README

````markdown
# Qwen3.6-27B-GGUF

Handles agentic coding and image-plus-text tasks locally, as Unsloth's GGUF files for Qwen3.6-27B with thinking preservation and a long context. For developers who run local coding agents.

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 is Unsloth's GGUF copy of Qwen3.6-27B, the first open-weight model in the Qwen3.6 line. It is a causal language model with a vision encoder, listed at 26.9B parameters. The Qwen team says it improves agentic coding, especially frontend workflows and reasoning across a whole repository, and adds an option to keep reasoning from earlier messages.

Who it suits: It suits developers who want a local model for agentic coding, and teams who work through long edit sessions where earlier reasoning should carry over.

```table
Industry | Who uses it | How they use it | Example
Software | Developers | Run a local coding agent model in Codex or OpenCode | Work on a frontend task in a repository
Product teams | Engineering teams | Keep reasoning context across iterative edits | Continue a long change over many messages
Research | ML researchers | Study a hybrid of Gated DeltaNet and attention layers | Read the layer layout in the card
```

Anyone without room for about 13.4 GB of 4-bit weights can skip it.

Where to use it:

- llama.cpp and other tools that read GGUF files
- Codex and OpenCode, since the card adds developer role support for them
- Unsloth Studio, where the card says Qwen3.6 can be run and fine-tuned
- Windows, macOS or Linux, where the files download with Python

```table
Use case | Who | What they get back
Agentic coding | Developers | Code and tool calls for frontend and repository work
Iterative development | Teams | Replies that build on reasoning kept from earlier turns
Long documents | Analysts | Answers over a context of up to 262,144 tokens
```

How to set it up:

- Python with pip, for the download
- A runtime that can load GGUF files
- Memory for about 13.4 GB at 4-bit, as an estimate

1. Install the Hugging Face hub client.
2. Download the repository's files.
3. Load a file in your runtime and follow Unsloth's guide for settings.

```sh
pip install -U huggingface_hub
hf download unsloth/Qwen3.6-27B-GGUF
```

How to use it: Load a quantization and send it coding tasks or questions with images. Keep the context window at 128K tokens or more, because the card says shorter windows weaken its thinking. If memory runs out, shrink the window first. For heavy production serving, the Qwen team recommends engines such as SGLang, KTransformers or vLLM.

```table
Tool | What it does
Thinking preservation | Keeps reasoning from earlier messages in the context
Developer role support | Lets Qwen3.6 work inside Codex, OpenCode and similar tools
Unsloth Studio | Runs and fine-tunes the model in a desktop interface
```

Example: You give the model a frontend task in a repository and keep the conversation going over several edits. With thinking preservation on, its earlier reasoning stays available as the work continues.

For newcomers and experts: Start with a 4-bit file and a short coding question. Add images or long conversations once the basics work.

The language model has 64 layers and a hidden width of 5120. Its layout repeats three Gated DeltaNet plus feed-forward layers, then one Gated Attention plus feed-forward layer, sixteen times. Gated Attention uses 24 query heads and 4 key-value heads. The card says it was trained with multi-step MTP.

```flow
A task and its history go in
The vision encoder reads any images
The hybrid stack reasons over a long context
Earlier reasoning can be kept
Code or an answer comes out
```

```chart
Title: Context length in tokens
Native context: 262,144 tokens
Extended context: 1,010,000 tokens
```

Good to know:

- Context: 262,144 tokens natively and extensible to 1,010,000
- Size: 26.9B parameters in the record, with about 13.4 GB at 4-bit as an estimate
- Tool calling: Unsloth says nested objects now parse better, so calls succeed more often
- License: Apache 2.0
- Cost to run: none named
- Data it reaches: the text and images you give it, with no web or account access named
- Not for: nothing named in the card

## On Hugging Face, 13 November 2026

- Task: image text to text
- Library: transformers
- Parameters: 26.9B
- Weights: about 13.4 GB at 4-bit (estimate)
- Formats: GGUF
- Downloads in the last 30 days: 227,770
- Likes: 978
- License: apache-2.0
- Based on: Qwen/Qwen3.6-27B
- Tags: transformers, gguf, unsloth, qwen, qwen3_5, image-text-to-text, imatrix, conversational

## Links

- Model: https://huggingface.co/unsloth/Qwen3.6-27B-GGUF
- Files: https://huggingface.co/unsloth/Qwen3.6-27B-GGUF/tree/main

## Get the model

Download its files with Hugging Face's command line:

```sh
pip install -U huggingface_hub
hf download unsloth/Qwen3.6-27B-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/Qwen3.6-27B-GGUF on Hugging Face](https://huggingface.co/unsloth/Qwen3.6-27B-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 |
| --- | --- | --- | --- | --- | --- |
| Qwen3.6-27B-GGUF (this page) | 227,770 | 978 | 26.9B | apache-2.0 | 2026-04-22 |
| [Qwen3.6-27B-MTP-GGUF](https://mindket.com/p/0818f9cc-d315-4c72-8def-581001d2b050) | 46,035 | 1,344 | 27.3B | apache-2.0 | 2026-05-26 |
| [Qwen3.6-35B-A3B-MTP-GGUF](https://mindket.com/p/90db7fe0-7044-4238-a508-5d2ecb760999) | 2,315,167 | 1,051 | 35.5B | apache-2.0 | 2026-05-20 |
| [Qwen3.6-35B-A3B-GGUF](https://mindket.com/p/9ac02ca9-6ba3-4ea0-b3f6-dcaea6e7789f) | 1,649,871 | 1,789 | 34.7B | apache-2.0 | 2026-04-20 |
| [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 |
| [Qwen3.5-4B-MTP-GGUF](https://mindket.com/p/dd8b24c6-73ed-4159-b540-b7370a9077f5) | 109,500 | 112 | 4.3B | apache-2.0 | 2026-05-16 |

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 · kevincox · 2026-07-29](https://news.ycombinator.com/item?id=49097944): Laguna S 2.1: This seems like a great option for local usage. With 16 GiB of VRAM on the official (https://huggingface.co/poolside/Laguna-S-2.1-GGUF) Q4\_K\_M quant (which seems to be recommended) I can get about 50 t/s in and 10 t/s out. Definitely not winning any speed benchmarks but totally usable for…
- [Bluesky · Alexis "xals" Lahouze · 2026-06-14](https://bsky.app/profile/xals.lahouze.org/post/3mobrury5rs2p): \`smolclaw\`, et \`llama.cpp\`, modèle \`unsloth/Qwen3.6-27B-GGUF\`, sur mon AMD Radeon RX 7900 XTX, je suis visiblement dans les 28 tokens par seconde en réponse. Je crois que je vais continuer à m'amuser dans cette configuration.
- [Hacker News · cpburns2009 · 2026-06-17](https://news.ycombinator.com/item?id=48575168): Ask HN: Has anyone replaced Claude/GPT with a local model for daily coding?: My LLM host is a Framework Desktop 128GB (Ryzen AI 395) with an RTX 5090 (Minisforum DEG1 dock, cheap Oculink card plugged into the PCIe 4.0 x4 slot). I run llama-server (b9296 compiled for CUDA) using a systemd service, and its configured as: \# server-2.env LLAMA\_ARGS="--host 0.0.0.0 --port 8082…
- [Bluesky · Tommi Somersuo · 2026-05-11](https://bsky.app/profile/tommis.fi/post/3mllof3z6bk2v): huggingface.co/unsloth/Qwen...
- [Hacker News · amboo7 · 2026-06-13](https://news.ycombinator.com/item?id=48517598): How to setup a local coding agent on macOS: Trying to recall... \# 27B GGUF \# Benchmark results: \# - TG speed: \~5 tok/s (vs baseline \~4.5 tok/s, +10% improvement) \# - PP speed: \~60 tok/s (stable across context sizes) \# - With parallel=4: total throughput \~28 tok/s llama-server \\ -m…
- [Bluesky · デイリーHuggingFaceトレンド · 2026-04-30](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mkpizjs3xn22): 今日のHuggingFaceトレンド unsloth/Qwen3.6-27B-GGUF Qwen3.6-27Bというポストトレーニング済みモデルの重みと設定ファイルを提供するためのリポジトリです。 Hugging Face Transformers形式で提供されており、vLLMやSGLangなどの推論フレームワークに対応しています。 安定性と実用性を重視し、開発者のコーディング体験の向上やツール呼び出し機能の改善を図っています。
- [Hacker News · xrd · 2026-04-30](https://news.ycombinator.com/item?id=47961615): Granite 4.1: IBM's 8B Model Matching 32B MoE: How do you run it? vllm? llama.cpp? Can you share some parameters you enable tool calling and agentic usage? Or, higher level, some philosophies on what approaches you are using for tuning to get better tool calling and/or agentic usage? I'm having surprisingly good success with…
- [Bluesky · デイリーHuggingFaceトレンド · 2026-04-28](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mkkiz3lo2u2r): 今日のHuggingFaceトレンド unsloth/Qwen3.6-27B-GGUF Qwen3.6-27Bのモデル重みと設定ファイルを公開するためのリポジトリです。 Hugging Face Transformers形式で提供され、vLLMやSGLangなどの推論フレームワークと互換性があります。 安定性と実用性を重視し、特に開発者のコーディング体験の向上やツール呼び出し機能の改善を目的としています。
- [Hacker News · xrd · 2026-04-27](https://news.ycombinator.com/item?id=47927592): Show HN: Utilyze – an open source GPU monitoring tool more accurate than nvtop: I feel like this is tangential to this conversation. Does anyone know of a good tool for "load balancing" usage across local GPUs? Why: I have two RTX3090s (24GB). I've been using nvidia-smi to check usage of my RTX3090. Mostly I'm running llama.cpp with unsloth/Qwen3.6-27B-GGUF:Q4\_K\_M and getting…
- [Bluesky · デイリーHuggingFaceトレンド · 2026-04-26](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mkfepq3qia2b): 今日のHuggingFaceトレンド unsloth/Qwen3.6-27B-GGUF Qwen3.6-27Bのポストトレーニング済みモデルの重みと設定ファイルを提供することが目的です。 Hugging Face Transformers形式で提供されており、vLLMやSGLangなどの推論フレームワークと互換性があります。 安定性と実用性を重視し、特にコーディング体験の向上やツール呼び出し機能の改善が図られています。 画像とテキストを入力とするマルチモーダルな機能も備えています。
- [Hacker News · fy20 · 2026-04-27](https://news.ycombinator.com/item?id=47925952): Running local LLMs offline on a ten-hour flight: Running it on a Macbook Pro M5 48GB: -hf unsloth/Qwen3.6-27B-GGUF:UD-Q6\_K\_XL \\ -c 128000 \\ --parallel 1 \\ --flash-attn on \\ --no-context-shift \\ --cache-type-k q8\_0 \\ --cache-type-v q8\_0 \\ --temp 0.6 \\ --top-p 0.95 \\ --top-k 20 \\ --min-p 0.0 \\ --presence\_penalty 0.0 \\ --reasoning on \\ --jinja \\…
- [Bluesky · デイリーHuggingFaceトレンド · 2026-04-24](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mkafksfmjw2r): 今日のHuggingFaceトレンド unsloth/Qwen3.6-27B-GGUF このリポジトリは、Hugging Face形式の学習済みモデルQwen3.6-27Bの重みと設定ファイルを提供することを目的としています。 開発者にとってより直感的で生産的なコーディング体験、安定性、および実用性の向上を目指しており、vLLMやSGLangなどの主要な推論フレームワークと互換性があります。 また、ツール呼び出し機能の改善も含まれています。

## 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: 2026-04-22
- First published: 2026-04-22
- Checked on Hugging Face: 2026-11-13
- Something wrong? Tell us: https://mindket.com/takedown#ask

## Get it

- [Open unsloth/Qwen3.6-27B-GGUF on Hugging Face](https://huggingface.co/unsloth/Qwen3.6-27B-GGUF)
- [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/91bfefb7-49da-47e4-a52a-f80091e27806)
- [Its page on Mindket](https://mindket.com/p/91bfefb7-49da-47e4-a52a-f80091e27806)