Name: Qwen3.6 27B NVFP4
Author: unsloth

Overview

Qwen3.6 27B NVFP4 is a high-efficiency multimodal model optimized for developers who need a balance between reasoning power and deployment speed. By utilizing NVFP4 quantization, this version significantly reduces memory overhead without sacrificing the core capabilities of the 27B parameter architecture, making it viable for consumer-grade GPUs or constrained cloud environments. It excels at image-to-text tasks, including complex visual reasoning, document parsing, and interleaved multimodal understanding. For developers, this means faster inference cycles and lower latency when integrating vision-language capabilities into RAG pipelines or automated content analysis tools. Compared to full-precision alternatives, it offers a streamlined path to production for high-throughput applications while maintaining the robust performance expected from the Qwen series.

Highlights

NVFP4 quantization for reduced VRAM and faster inference
Powerful multimodal reasoning across image and text inputs
Optimized for deployment on consumer-grade GPU hardware
Apache-2.0 license ensuring flexible commercial integration
High-throughput performance for vision-language RAG pipelines

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("unsloth/Qwen3.6-27B-NVFP4")
tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen3.6-27B-NVFP4")

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 unsloth/Qwen3.6-27B-NVFP4

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

huggingface-cli download unsloth/Qwen3.6-27B-NVFP4 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('unsloth/Qwen3.6-27B-NVFP4')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/unsloth/Qwen3.6-27B-NVFP4

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/unsloth/Qwen3.6-27B-NVFP4

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('unsloth/Qwen3.6-27B-NVFP4')
tokenizer = AutoTokenizer.from_pretrained('unsloth/Qwen3.6-27B-NVFP4')

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 unsloth/Qwen3.6-27B-NVFP4

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

modelscope download --model unsloth/Qwen3.6-27B-NVFP4 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('unsloth/Qwen3.6-27B-NVFP4')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://www.modelscope.cn/unsloth/Qwen3.6-27B-NVFP4.git

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/unsloth/Qwen3.6-27B-NVFP4.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', 'unsloth/Qwen3.6-27B-NVFP4')

Full Documentation

来源: HuggingFace

---
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
base_model:

Qwen/Qwen3.6-27B

tags:

unsloth

qwen

qwen3_5

---

Read our How to Run Qwen3.6 NVFP4 Guide!

<div>
<p style="margin: 0 0 0px 0; margin-top: 0px;">
<em>See <a href="https://unsloth.ai/docs/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0 GGUFs</a> for our quantization benchmarks.</em>
</p>
<div style="display: flex; gap: 5px; align-items: center; margin-bottom: 0px;">
<a href="https://github.com/unslothai/unsloth/">
<img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
</a>
<a href="https://discord.gg/unsloth">
<img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
</a>
<a href="https://unsloth.ai/docs/models/qwen3.6">
<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
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2.5x faster throughput than other NVFP4 quants.

This is an Unsloth NVFP4 quantized checkpoint calibrated on a mixture of our Unsloth dataset + UltraChat dataset.

Works on a 24GB VRAM GPU. Benchmarks on 1xB200 128 concurrency.

NVFP4 Accuracy Benchmarks

For accuracy benchmarks, we conducted MMLU-Pro, AIME 2025, GPQA for FP8, BF16, NVIDIA's NVFP4 and our NVFP4s - we show our faster quants do similarly on all:

| Provider | MMLU-Pro | GPQA | AIME 2025 |
| -------- | -------: | ----: | --------: |
| Unsloth NVFP4 | 86.25 | 86.34 | 93.12 |
| NVIDIA NVFP4 | 85.96 | 86.87 | 93.12 |
| FP8 | 86.11 | 86.87 | 93.75 |
| BF16 | 85.96 | 88.13 | 93.33 |

Read all benchmarks in our NVFP4 blog

vLLM Run Instructions

To install vLLM in a separate venv:

shell

uv venv unsloth-nvfp4-env --python 3.13
source unsloth-nvfp4-env/bin/activate
uv pip install "vllm>=0.25.0" "flashinfer-python>=0.6.13" "nvidia-cutlass-dsl>=4.5.2" \
    --torch-backend=auto

Then to serve the 27B NVFP4 quant:

code

vllm serve unsloth/Qwen3.6-27B-NVFP4 \
    --speculative-config '{"method": "mtp", "num_speculative_tokens": 2}'

Also do NOT use the Marlin backend since it's 2x slower - use the native vLLM or cute-DSL / CUTLASS / flashinfer_trtllm backends!
| Model | backend | decode tok/s | thr out tok/s |
|---|---|---|---|
| nvidia 27B | marlin (auto) | 115.6 | 2,403 |
| unsloth 27B | cute-DSL (auto) | 125.9 | 6,863 |
| nvidia 35B-A3B | marlin (auto) | 240.8 | 8,721 |
| unsloth 35B-A3B | cute-DSL + trtllm (auto) | 295.2 | 15,636 |

DGX Spark

You must use the below or you will get 2x slower inference!

shell

export CUTE_DSL_ARCH=sm_121a
vllm serve unsloth/Qwen3.6-27B-NVFP4 --moe-backend flashinfer_b12x

Multi-Token Prediction (MTP)

This checkpoint includes the MTP module, so it can act as its own speculative draft for faster decoding, but somewhat less throughput

shell

vllm serve unsloth/Qwen3.6-27B-NVFP4 \
    --speculative-config '{"method": "mtp", "num_speculative_tokens": 2}'

![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.

Original Qwen3.6 BF16 Reference Benchmarks

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="