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Open-weight model · Image and text to text

Huihui-Qwen3.6-27B-abliterated-AWQ-MTP

by Shawn Wei shawnw3i/Huihui-Qwen3.6-27B-abliterated-AWQ-MTP

This is an uncensored version of Qwen/Qwen3.6-27B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.

Parameters6.3B
Context262,144
Weights19.5 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads838.6k

Runs On

What it takes to serve Huihui-Qwen3.6-27B-abliterated-AWQ-MTP (6.3B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 12.6 GB 15.1 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 6.3 GB 7.5 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 3.1 GB 3.8 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

Model Card

By Shawn Wei, published under apache-2.0, revision ed099273cf30.

This is an uncensored version of Qwen/Qwen3.6-27B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. - AWQ Marlin kernel supported (auto-converted by vLLM at runtime) - MTP speculative decoding supported out of the box - 110+ tok/s on a single A800 80GB (vLLM 0.21.0, MTP enabled, fp8 KV cache) - Risk of Sensitive or Controversial Outputs: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated…

Read Shawn Wei's full model card

This is an uncensored version of Qwen/Qwen3.6-27B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.

Highlights

  • AWQ Marlin kernel supported (auto-converted by vLLM at runtime)
  • MTP speculative decoding supported out of the box
  • 110+ tok/s on a single A800 80GB (vLLM 0.21.0, MTP enabled, fp8 KV cache)

vLLM

vllm serve shawnw3i/Huihui-Qwen3.6-27B-abliterated-AWQ-MTP \
  --max-model-len 65536 \
  --reasoning-parser qwen3 \
  --speculative-config '{"method":"mtp","num_speculative_tokens":3}'

Usage Warnings

  • Risk of Sensitive or Controversial Outputs: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.

  • Not Suitable for All Audiences: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.

  • Legal and Ethical Responsibilities: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.

  • Research and Experimental Use: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.

  • Monitoring and Review Recommendations: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.

  • No Default Safety Guarantees: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.

Configuration

Architecture
Qwen3_5ForConditionalGeneration
Context length (tokens)
262,144
Layers
64
Hidden size
5,120
Feed-forward size
17,408
Attention heads
24
Key/value heads
4
Head dimension
256
Vocabulary size
248,320
Stored precision
float16
Model type
qwen3_5
Quantization
awq

Identity and Version

Repository
shawnw3i/Huihui-Qwen3.6-27B-abliterated-AWQ-MTP
Publisher
Shawn Wei
Task
Image and text to text
Modality
Image and text
Library
transformers
Parameters
6.3B parameters
Languages
awq, mtp
Revision
ed099273cf30ad72a88116d759856f147b7bcbff
First published
2026-04-26
Last updated
2026-05-22

Files and Weights

22 files, 19.6 GB in total. The weights are 11 files totalling 19.5 GB in safetensors.

Weights11 files · 19.5 GB
Configuration6 files · 209.2 KB
Tokenizer2 files · 20.0 MB
Documentation1 file · 2.4 KB
Other1 file · 7.8 KB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
model-00001-of-00010.safetensorsWeights2.1 GB 0899cdc88b12
model-00002-of-00010.safetensorsWeights2.1 GB ba314e1f5988
model-00003-of-00010.safetensorsWeights2.1 GB adb98e22d734
model-00004-of-00010.safetensorsWeights2.1 GB 3fcd21768611
model-00005-of-00010.safetensorsWeights2.1 GB 575749ceb279
model-00006-of-00010.safetensorsWeights2.1 GB 24733268ecda
model-00007-of-00010.safetensorsWeights800.6 MB 0cf2cb72a848
model-00008-of-00010.safetensorsWeights2.5 GB 523519d7fa60
model-00009-of-00010.safetensorsWeights10.3 KB e72381d38bbf
model-00010-of-00010.safetensorsWeights2.5 GB abed7b0a601b
model_extra_tensors.safetensorsWeights849.4 MB e5bb496d6658
config.jsonConfiguration10.1 KB
generation_config.jsonConfiguration213 B
model.safetensors.index.jsonConfiguration191.1 KB
preprocessor_config.jsonConfiguration443 B
processor_config.jsonConfiguration1.2 KB
quantization_config.jsonConfiguration6.2 KB
README.mdDocumentation2.4 KB
chat_template.jinjaOther7.8 KB
.gitattributesRepository1.6 KB
tokenizer.jsonTokenizer20.0 MB 06b9509352d2
tokenizer_config.jsonTokenizer1.2 KB

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
19.5 GB
Download from Shawn Wei

Released by Shawn Wei through its official repository on Hugging Face. Read the license.

Built From

Memory Requirements

PrecisionWeights in memory
As published19.5 GB
16-bit12.6 GB
8-bit6.3 GB
4-bit3.1 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About Huihui-Qwen3.6-27B-abliterated-AWQ-MTP

How much GPU memory does Huihui-Qwen3.6-27B-abliterated-AWQ-MTP need?

About 15.1 GB at 16-bit and 3.8 GB at 4-bit: the weights (6.3B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run Huihui-Qwen3.6-27B-abliterated-AWQ-MTP on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use Huihui-Qwen3.6-27B-abliterated-AWQ-MTP commercially?

Yes. Huihui-Qwen3.6-27B-abliterated-AWQ-MTP is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

What is Huihui-Qwen3.6-27B-abliterated-AWQ-MTP's context length?

262,144 tokens, from the maximum position embeddings in its published configuration.

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