license: apache-2.0
base_model:
- Nimbz/Gemma-4-Gembrain-31B
tags: - merge
- mergekit
- reasoning
- non-reasoning
- creative writing
- roleplay
- uncensored
- 31B
- gemma-4
- heretic
- uncensored
- decensored
- abliterated
- ara
This quant was made for and by Infermatic.ai
llmfan46/Gemma-4-Gembrain-31B-it-uncensored-heretic
Copy of the original card
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87% fewer refusals (13/100 Uncensored vs 99/100 Original) while preserving model quality (0.0186 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.
This is a decensored version of Nimbz/Gemma-4-Gembrain-31B, made using Heretic v1.2.0 with the Arbitrary-Rank Ablation (ARA) method
Abliteration parameters
| Parameter | Value |
|---|---|
| start_layer_index | 5 |
| end_layer_index | 60 |
| preserve_good_behavior_weight | 0.9776 |
| steer_bad_behavior_weight | 0.0002 |
| overcorrect_relative_weight | 1.0158 |
| neighbor_count | 15 |
Targeted components
- attn.o_proj
Performance
| Metric | This model | Original model (Gemma-4-Gembrain-31B) |
|---|---|---|
| KL divergence | 0.0186 | 0 (by definition) |
| Refusals | ✅ 13/100 | ❌ 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: 6084
Accuracy: 0.8665 (86.65%)
Parse failures: 51
============================================================
Tested subject scores:
- professional_law: 0.7694 (604/785)
- moral_scenarios: 0.8326 (368/442)
- miscellaneous: 0.9269 (355/383)
- professional_psychology: 0.9051 (286/316)
- high_school_psychology: 0.9667 (261/270)
- high_school_macroeconomics: 0.9289 (183/197)
- elementary_mathematics: 0.9511 (175/184)
- moral_disputes: 0.8563 (149/174)
- prehistory: 0.9360 (161/172)
- philosophy: 0.8616 (137/159)
- high_school_biology: 0.9539 (145/152)
- professional_accounting: 0.8392 (120/143)
- clinical_knowledge: 0.9143 (128/140)
- high_school_microeconomics: 0.9706 (132/136)
- nutrition: 0.9259 (125/135)
- professional_medicine: 0.9328 (125/134)
- conceptual_physics: 0.9219 (118/128)
- high_school_mathematics: 0.5748 (73/127)
- human_aging: 0.8448 (98/116)
- security_studies: 0.8839 (99/112)
- high_school_statistics: 0.8919 (99/111)
- marketing: 0.9725 (106/109)
- high_school_world_history: 0.9528 (101/106)
- sociology: 0.8932 (92/103)
- high_school_government_and_politics: 0.9703 (98/101)
- high_school_geography: 0.9293 (92/99)
- high_school_chemistry: 0.7732 (75/97)
- high_school_us_history: 0.9368 (89/95)
- virology: 0.4944 (44/89)
- college_medicine: 0.8523 (75/88)
- world_religions: 0.9091 (80/88)
- high_school_physics: 0.7857 (66/84)
- electrical_engineering: 0.8642 (70/81)
- astronomy: 0.9494 (75/79)
- logical_fallacies: 0.9079 (69/76)
- high_school_european_history: 0.8904 (65/73)
- anatomy: 0.8732 (62/71)
- college_biology: 0.9531 (61/64)
- human_sexuality: 0.9219 (59/64)
- formal_logic: 0.7969 (51/64)
- public_relations: 0.7377 (45/61)
- international_law: 0.9167 (55/60)
- college_physics: 0.6842 (39/57)
- college_mathematics: 0.7455 (41/55)
- econometrics: 0.7963 (43/54)
- jurisprudence: 0.8679 (46/53)
- high_school_computer_science: 0.9808 (51/52)
- machine_learning: 0.8462 (44/52)
- medical_genetics: 0.9608 (49/51)
- global_facts: 0.5490 (28/51)
- management: 0.9200 (46/50)
- us_foreign_policy: 0.9200 (46/50)
- college_chemistry: 0.5957 (28/47)
- abstract_algebra: 0.7660 (36/47)
- business_ethics: 0.8261 (38/46)
- college_computer_science: 0.9333 (42/45)
- computer_security: 0.8372 (36/43)
Heretic:
============================================================
Total questions: 7021
Correct: 6031
Accuracy: 0.8590 (85.90%)
Parse failures: 43
============================================================
Tested subject scores:
- professional_law: 0.7529 (591/785)
- moral_scenarios: 0.8009 (354/442)
- miscellaneous: 0.9269 (355/383)
- professional_psychology: 0.8924 (282/316)
- high_school_psychology: 0.9667 (261/270)
- high_school_macroeconomics: 0.9188 (181/197)
- elementary_mathematics: 0.9620 (177/184)
- moral_disputes: 0.8506 (148/174)
- prehistory: 0.9302 (160/172)
- philosophy: 0.8553 (136/159)
- high_school_biology: 0.9539 (145/152)
- professional_accounting: 0.8252 (118/143)
- clinical_knowledge: 0.9071 (127/140)
- high_school_microeconomics: 0.9632 (131/136)
- nutrition: 0.9111 (123/135)
- professional_medicine: 0.9179 (123/134)
- conceptual_physics: 0.9141 (117/128)
- high_school_mathematics: 0.5827 (74/127)
- human_aging: 0.8534 (99/116)
- security_studies: 0.8571 (96/112)
- high_school_statistics: 0.8649 (96/111)
- marketing: 0.9633 (105/109)
- high_school_world_history: 0.9528 (101/106)
- sociology: 0.9126 (94/103)
- high_school_government_and_politics: 0.9703 (98/101)
- high_school_geography: 0.9293 (92/99)
- high_school_chemistry: 0.7835 (76/97)
- high_school_us_history: 0.9158 (87/95)
- virology: 0.4944 (44/89)
- college_medicine: 0.8409 (74/88)
- world_religions: 0.9091 (80/88)
- high_school_physics: 0.7857 (66/84)
- electrical_engineering: 0.8519 (69/81)
- astronomy: 0.9494 (75/79)
- logical_fallacies: 0.9211 (70/76)
- high_school_european_history: 0.8904 (65/73)
- anatomy: 0.8592 (61/71)
- college_biology: 0.9531 (61/64)
- human_sexuality: 0.8906 (57/64)
- formal_logic: 0.7969 (51/64)
- public_relations: 0.7705 (47/61)
- international_law: 0.9167 (55/60)
- college_physics: 0.7018 (40/57)
- college_mathematics: 0.6909 (38/55)
- econometrics: 0.7963 (43/54)
- jurisprudence: 0.8491 (45/53)
- high_school_computer_science: 0.9808 (51/52)
- machine_learning: 0.8269 (43/52)
- medical_genetics: 0.9216 (47/51)
- global_facts: 0.6078 (31/51)
- management: 0.9200 (46/50)
- us_foreign_policy: 0.9600 (48/50)
- college_chemistry: 0.5745 (27/47)
- abstract_algebra: 0.7447 (35/47)
- business_ethics: 0.8261 (38/46)
- college_computer_science: 0.9111 (41/45)
- computer_security: 0.8372 (36/43)
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-Gembrain-31B-it-uncensored-heretic-GGUF.
💎 GEMBRAIN-31B 🧠
🧠 About The Model
Gembrain-31B is a synthesis of several models, including Gemsicle-31B as important ingredient. The goal of this release was to stabilize and improve the initial Gemsicle-31B, but also to enhance its logical and lateral thinking, both with and without reasoning.
It's build to create the most unhinged narratives and construct image prompts about anything accordingly to a given structure with high precision.
Expect creative swipe variance, unique and non-robotic prose, and sharper instruction adherence.
🎚️ Samplers
| Temperature | 1.0 |
| Top-K | 0 |
| Top-P | 0.95 |
| Min-P | 0.03 |
| DRY Multiplier | 0.8 |
| DRY Base | 1.75 |
| DRY Allowed Length | 10 |
| Optional: Adaptive-P Target | 0.6 |
| Optional: Adaptive-P Decay | 0.5 |
🔮 Prompt Format
Please refer to the original google/gemma-4-31b-it for the correct chat template.
Let your frontend handle the chat template if possible (e.g., Chat Completion in SillyTavern).
For Reasoning: Add <|think|> at the very beginning of the system prompt. Thinking happens between <|channel>thought\n and<channel|> tags.
<|turn>system
<|think|>
You are a helpful assistant<turn|>
<|turn>user
Hello<turn|>
<|turn>model
Hi there<turn|>
<|turn>user
How are you?<turn|>
<|turn>model
🧪 Merge Details
This model was systematically created through a five-stage process of priming models for their given purpose and merging the results:
Phase 01: breadcrumbs_ties
Gemopus X MeroMero
models:
- model: ./G4-MeroMero-31B
- model: ./G4-Gemopus-4-31B-it
merge_method: breadcrumbs_ties
base_model: ./G4-31B-it
parameters:
density: 0.85
weight: 0.5
int8_mask: true
dtype: bfloat16
Phase 02: slerp
GarnetV2 X Musica-v1
models:
- model: ./G4-Gemma4-GarnetV2-31B
- model: ./G4-31B-Musica-v1
merge_method: slerp
base_model: ./G4-Gemma4-GarnetV2-31B
parameters:
t:
- value: 0.6
dtype: bfloat16
Phase 03: della_linear
Gemsicle X Gemma-4-31B-it-heretic-ara
models:
- model: ./Gemsicle-31B
parameters:
weight: 1.0
- model: ./G4-gemma-4-31b-it-heretic-ara
parameters:
weight: 0.75
density: 0.65
merge_method: della_linear
base_model: ./G4-31B-it
parameters:
weight: 1.0
normalize: false
epsilon: 0.05
lambda: 1.0
dtype: bfloat16
Phase 04: model_stock
Phase 01 X Phase 02 X Phase 03
models:
- model: ./phase01_breadcrumbs_ties
- model: ./phase02_slerp
merge_method: model_stock
base_model: ./phase03_della_linear
dtype: bfloat16
tokenizer_source: "base"
Phase 05: arcee_fusion
Gemsicle X Phase 04
models:
- model: ./Gemsicle-31B
- model: ./phase04_model_stock
merge_method: arcee_fusion
base_model: ./Gemsicle-31B
dtype: bfloat16
tokenizer_source: "base"