llmfan46/Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic

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96% fewer refusals (4/100 Uncensored vs 99/100 Original) while preserving model quality (0.0167 KL divergence).

❤️ Support My Work

Creating these models takes significant time, work and compute. If you find them useful consider supporting me:

Platform	Link	What you get
🎉 Patreon	Monthly support	Priority model requests
☕ Ko-fi	One-time tip	My eternal gratitude

Your help will motivate me and would go into further improving my workflow and coverings fees for storage, compute and may even help uncensoring bigger model with rental Cloud GPUs.

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This model is great for creative writing and translation works that benefit from heightened diction and grandeur, the original base model gemma-4-31B-it writing and translations feels very stiff with some odd word choices that might not really fit very well the situation, Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic aims to fix this issue and improve the writing quality of Gemma 4 31B it.

This is a decensored version of ConicCat/Gemma4-GarnetV2-31B, made using Heretic v1.2.0 with the Arbitrary-Rank Ablation (ARA) method

Abliteration parameters

Parameter	Value
start_layer_index	25
end_layer_index	46
preserve_good_behavior_weight	0.4482
steer_bad_behavior_weight	0.0002
overcorrect_relative_weight	1.0104
neighbor_count	10

Targeted components

• attn.o_proj

Performance

Metric	This model	Original model (Gemma4-GarnetV2-31B)
KL divergence	0.0167	0 (by definition)
Refusals	✅ 4/100	❌ 99/100

Lower refusals indicate fewer content restrictions, while lower KL divergence indicates more closeness to the original model's baseline. Higher refusals cause more rejections, objections, pushbacks, lecturing, censorship, softening and deflections.

MMLU test results:

Original:

============================================================

• Total questions: 7021

• Correct: 5955

• Accuracy: 0.8482 (84.82%)

• Parse failures: 67

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Top subjects:

• professional_law: 0.7350 (577/785)
• moral_scenarios: 0.8348 (369/442)
• miscellaneous: 0.9243 (354/383)
• professional_psychology: 0.8734 (276/316)
• high_school_psychology: 0.9630 (260/270)
• high_school_macroeconomics: 0.9086 (179/197)
• prehistory: 0.8837 (152/172)
• moral_disputes: 0.8448 (147/174)
• elementary_mathematics: 0.9076 (167/184)
• philosophy: 0.8428 (134/159)

Heretic:

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• Total questions: 7021

• Correct: 5893

• Accuracy: 0.8393 (83.93%)

• Parse failures: 49

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Top subjects:

• professional_law: 0.7083 (556/785)
• moral_scenarios: 0.8167 (361/442)
• miscellaneous: 0.9112 (349/383)
• professional_psychology: 0.8639 (273/316)
• high_school_psychology: 0.9593 (259/270)
• high_school_macroeconomics: 0.9036 (178/197)
• prehistory: 0.8837 (152/172)
• moral_disputes: 0.8276 (144/174)
• elementary_mathematics: 0.9130 (168/184)
• philosophy: 0.8113 (129/159)

MMLU - Massive Multitask Language Understanding, multiple-choice questions across 57 subjects (math, history, law, medicine, etc.).

GGUF Version

GGUF quantizations available here llmfan46/Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic-GGUF.

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ConicCat/Gemma4-GarnetV2-31B

A finetune primarily focused on improving the prose and writing capabilities of Gemma 4. This does generalize strongly to roleplay and most other creative domains as well.

Features:

• Improved longform writing capabilites; output context extension allows for prompting for up to 4000 words of text in one go.
• Markedly less AI slop and identifiable Gemini-isms in writing.
• Improved swipe or output diversity.
• Fewer 'soft' refusals in writing.

Difference from V1

More / better roleplay data as well as shifting to using more primarily fantasy and sci fi books for training over literary fiction.

Datasets

• internlm/Condor-SFT-20K for instruct; even though instruct capabilities are not the primary focus, adding some instruct data helps mitigate forgetting and maintains general intellect and instruction following capabilites.
• ConicCat/Gutenberg-SFT. A reformatted version of the original Gutenberg DPO dataset by jondurbin for SFT with some slight augmentation to address many of the samples being overly long.
• A dataset of backtranslated books. Unfortunately, I am unable to release this set as all of the data is under copyright.
• A dash of a certain third owned archive.