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Open-weight model · Text generation

Qwen3.6-35B-A3B-abliterated-v4

by CS Bahushruth/Qwen3.6-35B-A3B-abliterated-v4

Qwen3.6-35B-A3B-abliterated-v4 is an open-weight model for text generation from CS, released under Apache License 2.0. It has 34.7B parameters and a 262,144-token context. At 16-bit it needs about 83.2 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 896.6k downloads a month.

Uncensored version of Qwen/Qwen3.6-35B-A3B with refusal behavior removed via abliteration (norm-preserving orthogonalization). Zero refusals on harmful prompts. No false refusals on harmless prompts.

Parameters34.7B
Context262,144
Weights69.3 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads896.6k

Runs On

What it takes to serve Qwen3.6-35B-A3B-abliterated-v4 (34.7B 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 69.3 GB 83.2 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x MI325X $2.00 · 1x MI355X $2.59
8-bit 34.7 GB 41.6 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 17.3 GB 20.8 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 Oct 16, 2026.

Qwen3.6-35B-A3B-abliterated-v4 on every accelerator the SAVRN Index prices, at every precision

SAVRN's Notes on Qwen3.6-35B-A3B-abliterated-v4

83.2 GB at 16-bit, 20.8 GB at 4-bit for a 34.7B-parameter mixture-of-experts with 262,144 tokens of context: the whole thing fits one 192 GB MI300X at $1.85 an hour, with headroom the long-context cache will happily consume. That is the practical news — a quarter-million-token window usually means a multi-GPU bill, and here it does not. A GGUF conversion of the same checkpoint sits in a sibling repository if your stack is llama.cpp rather than vLLM.

What you are actually downloading is Qwen/Qwen3.6-35B-A3B with its refusal direction surgically removed, per the publisher's own table: harmful-prompt refusals drop from roughly 98% to zero, with harmless-prompt refusals unchanged at zero. The refusal policy moves out of the weights and into your gateway, which is where an operator has to own it. Apache 2.0 keeps commercial use open, and no third-party evaluations are reported for this edit, so budget time to regression-test it yourself.

SAVRN Research

Model Card

By CS, published under apache-2.0, revision 1c28f5014d7e.

Uncensored version of Qwen/Qwen3.6-35B-A3B with refusal behavior removed via abliteration (norm-preserving orthogonalization).

GGUF quantizations: Bahushruth/Qwen3.6-35B-A3B-abliterated-v4-GGUF

Blog post: Abliteration: Uncensoring LLMs via Weight Surgery

Results

Metric Original v4 (this model)
Harmful refusal rate ~98% 0% (0/16)
Harmless prompt refusal 0% 0%

Zero refusals on harmful prompts. No false refusals on harmless prompts.

Method

Abliteration identifies the "refusal direction" in the model's residual stream — the linear direction that activates when the model decides to refuse — and surgically removes it from all output projection weights using norm-preserving orthogonalization.

Parameter Value
Directions removed 1 (single top layer)
Orthogonalization Norm-preserving (grimjim method)
Harmful dataset Bahushruth/abliteration-harmful-enriched (7356 prompts, 33 categories)
Harmless dataset mlabonne/harmless_alpaca
Training samples 512 pairs
Compute Modal H100 (80GB VRAM, 128GB RAM)

Algorithm

Read the full model card (544 words)

Configuration

Architecture
Qwen3_5MoeForCausalLM
Context length (tokens)
262,144
Layers
40
Hidden size
2,048
Attention heads
16
Key/value heads
2
Head dimension
256
Vocabulary size
248,320
Experts
256
Experts active per token
8
Model type
qwen3_5_moe_text

Identity and Version

Repository
Bahushruth/Qwen3.6-35B-A3B-abliterated-v4
Publisher
CS
Task
Text generation
Modality
Text
Library
transformers
Parameters
34.7B parameters
Languages
moe
Revision
1c28f5014d7e9c20170af40e5aa028679fb30bfb
First published
2026-06-11
Last updated
2026-07-03

Files and Weights

10 files, 69.3 GB in total. The weights are 2 files totalling 69.3 GB in safetensors.

Weights2 files · 69.3 GB
Configuration3 files · 72.2 KB
Tokenizer2 files · 20.0 MB
Documentation1 file · 5.8 KB
Other1 file · 7.8 KB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
model-00001-of-00002.safetensorsWeights49.7 GB c3f29d1e6352
model-00002-of-00002.safetensorsWeights19.6 GB d31ad663b23a
config.jsonConfiguration2.3 KB —
generation_config.jsonConfiguration214 B —
model.safetensors.index.jsonConfiguration69.7 KB —
README.mdDocumentation5.8 KB —
chat_template.jinjaOther7.8 KB —
.gitattributesRepository1.6 KB —
tokenizer.jsonTokenizer20.0 MB 68ec2440b89e
tokenizer_config.jsonTokenizer1.1 KB —

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
69.3 GB
Download from CS

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

Built From

  • Derived from Qwen/Qwen3.6-35B-A3B
  • Trained on (disclosed) Bahushruth/abliteration-harmful-enriched
  • Trained on (disclosed) mlabonne/harmless_alpaca

Memory Requirements

PrecisionWeights in memory
As published69.3 GB
16-bit69.3 GB
8-bit34.7 GB
4-bit17.3 GB

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

Compare Qwen3.6-35B-A3B-abliterated-v4

Questions About Qwen3.6-35B-A3B-abliterated-v4

How much GPU memory does Qwen3.6-35B-A3B-abliterated-v4 need?

About 83.2 GB at 16-bit and 20.8 GB at 4-bit: the weights (34.7B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run Qwen3.6-35B-A3B-abliterated-v4 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 Qwen3.6-35B-A3B-abliterated-v4 commercially?

Yes. Qwen3.6-35B-A3B-abliterated-v4 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 Qwen3.6-35B-A3B-abliterated-v4's context length?

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

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