Name: Qwen3.6 27B AWQ INT4
Author: cyankiwi

Overview

Qwen3.6 27B AWQ INT4 is a quantized multimodal model designed for developers needing a high-performance balance between reasoning capabilities and VRAM efficiency. By utilizing 4-bit AWQ quantization, this version significantly lowers the hardware barrier for deploying a 27B parameter model without substantial loss in perplexity or accuracy. It excels in vision-language tasks, allowing for seamless integration into pipelines that require complex image analysis, document parsing, and interleaved text-image reasoning. For developers, this means faster inference speeds and the ability to run the model on consumer-grade GPUs or tighter cloud instances compared to the full-precision weights, making it an ideal candidate for production-ready RAG applications and multimodal agents.

Highlights

4-bit AWQ quantization reduces VRAM overhead for faster deployment.
Strong multimodal capabilities for image-to-text and visual reasoning.
Optimized for high-throughput inference on consumer-grade hardware.
Apache-2.0 license ensures flexible commercial integration.
Maintains 27B parameter reasoning power with reduced memory footprint.

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("cyankiwi/Qwen3.6-27B-AWQ-INT4")
tokenizer = AutoTokenizer.from_pretrained("cyankiwi/Qwen3.6-27B-AWQ-INT4")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download cyankiwi/Qwen3.6-27B-AWQ-INT4

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download cyankiwi/Qwen3.6-27B-AWQ-INT4 config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('cyankiwi/Qwen3.6-27B-AWQ-INT4')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/cyankiwi/Qwen3.6-27B-AWQ-INT4

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/cyankiwi/Qwen3.6-27B-AWQ-INT4

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('cyankiwi/Qwen3.6-27B-AWQ-INT4')
tokenizer = AutoTokenizer.from_pretrained('cyankiwi/Qwen3.6-27B-AWQ-INT4')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance

pip install modelscope

CLI Download

Download the full repository

modelscope download --model cyankiwi/Qwen3.6-27B-AWQ-INT4

Download a single file to a local folder (e.g. README.md into ./dir)

modelscope download --model cyankiwi/Qwen3.6-27B-AWQ-INT4 README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('cyankiwi/Qwen3.6-27B-AWQ-INT4')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://www.modelscope.cn/cyankiwi/Qwen3.6-27B-AWQ-INT4.git

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/cyankiwi/Qwen3.6-27B-AWQ-INT4.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'cyankiwi/Qwen3.6-27B-AWQ-INT4')

Full Documentation

来源: HuggingFace

---
base_model: Qwen/Qwen3.6-27B
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3.6-27B/blob/main/LICENSE
pipeline_tag: image-text-to-text
---

<div align="left">
<table align="center" style="border-collapse:collapse; border:none;">
<tr style="border:none;">
<td align="right" style="border:none; padding:4px 12px 4px 0;"><b>Version</b></td>
<td align="left" style="border:none; padding:4px 0;">26.05.01</td>
</tr>
<tr style="border:none;">
<td align="right" style="border:none; padding:4px 12px 4px 0;"><b>Calibration</b></td>
<td align="left" style="border:none; padding:4px 0;">
<a href="https://huggingface.co/datasets/cyankiwi/calibration" target="_blank">STEM and Agentic</a>
</td>
</tr>
<tr style="border:none;">
<td align="right" style="border:none; padding:4px 12px 4px 0;"><b>Languages</b></td>
<td align="left" style="border:none; padding:4px 0;">
<code>EN</code> <code>ZH</code> <code>HI</code> <code>AR</code> <code>RU</code>
<code>JA</code> <code>KO</code> <code>NL</code> <code>FR</code> <code>ES</code>
</td>
</tr>
<tr style="border:none;">
<td align="right" style="border:none; padding:4px 12px 4px 0;"><b>Model Size</b></td>
<td align="left" style="border:none; padding:4px 0;">20.44 GB</td>
</tr>
<tr style="border:none;">
<td align="right" style="border:none; padding:4px 12px 4px 0;"><b>Contact</b></td>
<td align="left" style="border:none; padding:4px 0;">
<a href="mailto:[email protected]">Email</a>
</td>
</tr>
</table>
</div>

---

Qwen3.6-27B

![Qwen Chat](https://chat.qwen.ai)

> [!Note]
> This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format.
>
> These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.

Following the February release of the Qwen3.5 series, we're pleased to share the first open-weight variant of Qwen3.6. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience.

Qwen3.6 Highlights

This release delivers substantial upgrades, particularly in

Agentic Coding: the model now handles frontend workflows and repository-level reasoning with greater fluency and precision.

Thinking Preservation: we've introduced a new option to retain reasoning context from historical messages, streamlining iterative development and reducing overhead.

!Benchmark Results

For more details, please refer to our blog post Qwen3.6-27B.

Model Overview

Type: Causal Language Model with Vision Encoder

Training Stage: Pre-training & Post-training

Language Model

- Number of Parameters: 27B - Hidden Dimension: 5120 - Token Embedding: 248320 (Padded) - Number of Layers: 64 - Hidden Layout: 16 × (3 × (Gated DeltaNet → FFN) → 1 × (Gated Attention → FFN)) - Gated DeltaNet: - Number of Linear Attention Heads: 48 for V and 16 for QK - Head Dimension: 128 - Gated Attention: - Number of Attention Heads: 24 for Q and 4 for KV - Head Dimension: 256 - Rotary Position Embedding Dimension: 64 - Feed Forward Network: - Intermediate Dimension: 17408 - LM Output: 248320 (Padded) - MTP: trained with multi-steps

Context Length: 262,144 natively and extensible up to 1,010,000 tokens.

Benchmark Results

Language

<div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;max-width:1000px;margin:0 auto;padding:16px 0">
<table style="width:100%;border-collapse:collapse;font-size:13px">
<thead><tr>
<th style="padding:10px 7px;text-align:left;font-weight:600;border-bottom:2px solid #7c3aed;color:#7c3aed"></th><th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Qwen3.5-27B</th><th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Qwen3.5-397B-A17B</th><th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Gemma4-31B</th><th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Claude 4.5 Opus</th><th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Qwen3.6-35B-A3B</th><th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size: 14px;">Qwen3.6-27B</th></tr></thead>
<tbody>
<tr><td colspan="7" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124, 58, 237, 0.2);background:rgba(124, 58, 237, 0.1)">Coding Agent</td></tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Verified</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">75.0</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.2</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">52.0</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.9</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">73.4</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.2</td>
</tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Pro</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">51.2</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">50.9</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">35.7</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">57.1</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">49.5</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">53.5</td>
</tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Multilingual</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.3</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.3</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">51.7</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">77.5</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.2</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">71.3</td>
</tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Terminal-Bench 2.0</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">41.6</td>
<td style="padding:7