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Open-weight model · Any to any

Huihui-gemma-4-12B-it-abliterated

by Isaac Jaxxon TimelessSoul/Huihui-gemma-4-12B-it-abliterated

Huihui-gemma-4-12B-it-abliterated is an open-weight model for any to any from Isaac Jaxxon, released under Apache License 2.0. It has 12B parameters and a 131,072-token context. At 16-bit it needs about 28.7 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.

This is an uncensored version of google/gemma-4-12B-it created with abliteration (see remove-refusals-with-transformers to know more about it).

Parameters12B
Context131,072
Weights23.9 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—

Runs On

What it takes to serve Huihui-gemma-4-12B-it-abliterated (12B 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 23.9 GB 28.7 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 12.0 GB 14.4 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 6.0 GB 7.2 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 18, 2026.

Huihui-gemma-4-12B-it-abliterated on every accelerator the SAVRN Index prices, at every precision

Model Card

By Isaac Jaxxon, published under apache-2.0, revision b4e4d2481d2e.

This is an uncensored version of google/gemma-4-12B-it 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. Note For this model, both the thinking mode and the non-thinking mode have been completely abliterated. Only layers 23-28 have been abliterated. Important Notice: The weights of the earliest released google/gemma-4-12B-it model have issues. We have already re-ablated and re-uploaded the model. If you have already downloaded it, please re-download. Please use the latest version of ollama You can use huihuiai/gemma-4-abliterated:12b…

Read Isaac Jaxxon's full model card

huihui-ai/Huihui-gemma-4-12B-it-abliterated

This is an uncensored version of google/gemma-4-12B-it 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.

Note For this model, both the thinking mode and the non-thinking mode have been completely abliterated. Only layers 23-28 have been abliterated.

Important Notice: The weights of the earliest released google/gemma-4-12B-it model have issues. We have already re-ablated and re-uploaded the model. If you have already downloaded it, please re-download.

ollama

Please use the latest version of ollama

You can use huihui_ai/gemma-4-abliterated:12b directly, 

ollama run huihui_ai/gemma-4-abliterated:12b

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.

Donation

Your donation helps us continue our further development and improvement, a cup of coffee can do it.
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Configuration

Architecture
Gemma4UnifiedForConditionalGeneration
Context length (tokens)
131,072
Layers
48
Hidden size
3,840
Feed-forward size
15,360
Attention heads
16
Key/value heads
8
Head dimension
256
Vocabulary size
262,144
Sliding window (tokens)
1,024
Model type
gemma4_unified

Identity and Version

Repository
TimelessSoul/Huihui-gemma-4-12B-it-abliterated
Publisher
Isaac Jaxxon
Task
Any to any
Modality
Multimodal
Library
transformers
Parameters
12B parameters
Languages
Not stated by the source
Revision
b4e4d2481d2ec02825165e7a967644c5d2522c26
First published
2026-10-07
Last updated
2026-10-07

Files and Weights

9 files, 24.0 GB in total. The weights are 1 file totalling 23.9 GB in safetensors.

Weights1 file · 23.9 GB
Configuration3 files · 6.1 KB
Tokenizer2 files · 32.2 MB
Documentation1 file · 2.8 KB
Other1 file · 17.5 KB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
model.safetensorsWeights23.9 GB 2dd13d86762e
config.jsonConfiguration4.4 KB —
generation_config.jsonConfiguration260 B —
processor_config.jsonConfiguration1.4 KB —
README.mdDocumentation2.8 KB —
chat_template.jinjaOther17.5 KB —
.gitattributesRepository1.6 KB —
tokenizer.jsonTokenizer32.2 MB cc8d3a0ce364
tokenizer_config.jsonTokenizer2.1 KB —

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
23.9 GB
Download from Isaac Jaxxon

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

Built From

Memory Requirements

PrecisionWeights in memory
As published23.9 GB
16-bit23.9 GB
8-bit12.0 GB
4-bit6.0 GB

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

Questions About Huihui-gemma-4-12B-it-abliterated

How much GPU memory does Huihui-gemma-4-12B-it-abliterated need?

About 28.7 GB at 16-bit and 7.2 GB at 4-bit: the weights (12B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run Huihui-gemma-4-12B-it-abliterated 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-gemma-4-12B-it-abliterated commercially?

Yes. Huihui-gemma-4-12B-it-abliterated 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-gemma-4-12B-it-abliterated's context length?

131,072 tokens, from the maximum position embeddings in its published configuration.

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