---
license: apache-2.0
base_model:
- llmfan46/Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic
pipeline_tag: text-generation
datasets:
- ConicCat/Gutenberg-SFT
- ConicCat/Condor-SFT-Filtered
tags:
- gemma4
- heretic
- uncensored
- decensored
- abliterated
- ara
---
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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:

![image/png](https://huggingface.co/llmfan46/Omega-Darker-Gaslight_The-Final-Forgotten-Fever-Dream-24B-ultra-uncensored-heretic-v1/resolve/main/waifu001.webp)

| Platform | Link | What you get |
|----------|------|--------------|
| 🎉 Patreon | [Monthly support](https://patreon.com/LLMfan46) | Priority model requests |
| ☕ Ko-fi | [One-time tip](https://ko-fi.com/llmfan46) | 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.

-----

GGUF quantizations of [llmfan46/Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic](https://huggingface.co/llmfan46/Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic).

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](https://huggingface.co/ConicCat/Gemma4-GarnetV2-31B), made using [Heretic](https://github.com/p-e-w/heretic) v1.2.0 with the [Arbitrary-Rank Ablation (ARA)](https://github.com/p-e-w/heretic/pull/211) 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](https://huggingface.co/ConicCat/Gemma4-GarnetV2-31B)) |
| :----- | :--------: | :---------------------------: |
| **KL divergence** | <span style="color:darkgoldenrod">0.0167</span> | 0 *(by definition)* |
| **Refusals** | ✅ <span style="color:darkgreen">4/100</span> | ❌ <span style="color:blue">99/100</span> |

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:

<span style="color:blue">Original:</span>

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

- Total questions: 7021

- Correct: 5955

- Accuracy: 0.8482 (84.82%)

- Parse failures: 67

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

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)


<span style="color:darkgreen">Heretic:</span>

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

- Total questions: 7021

- Correct: 5893

- Accuracy: 0.8393 (83.93%)

- Parse failures: 49

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

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.).

-----

## Quantizations

| Filename | Quant | Description |
|----------|-------|-------------|
| Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic-BF16.gguf | BF16 | Full precision |
| Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic-Q8_0.gguf | Q8_0 | Near-lossless, recommended |
| Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic-Q6_K.gguf | Q6_K | Excellent quality |
| Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic-Q5_K_M.gguf | Q5_K_M | Good balance |
| Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic-Q5_K_S.gguf | Q5_K_S | Smaller Q5 |
| Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic-Q4_K_M.gguf | Q4_K_M | Good for limited VRAM |
| Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic-Q4_K_S.gguf | Q4_K_S | Smaller Q4 |
| Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic-Q3_K_L.gguf | Q3_K_L | Low VRAM, decent quality 
| Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic-Q3_K_M.gguf | Q3_K_M | Low VRAM, smaller |

## Vision Projector

| Filename | Quant | Description |
|----------|-------|-------------|
| Gemma-4-Garnet-V2-31B-it-mmproj-BF16.gguf | BF16 | Native precision |

A Vision Projector File is Required for vision/multimodal capabilities. Use alongside any quantization above.

## Usage

Works with llama.cpp, LM Studio, Ollama, and other GGUF-compatible tools.

-----

# 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.