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

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I can no longer upload new models unless I can cover the cost of additional storage.
I host 70+ free models as an independent contributor and this work is unpaid.
Without your support, no more new models can be uploaded.

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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:

image/png

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 🧠

INSANE IN THE GEMBRAIN

ADHERENCEIMPROVED

SWIPE VARIETYINCREASED

CREATIVE PROSEPRESERVED

🧠 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

🫟 GGUF Quants

Quant	Size	Download Link
Q4_K_S	`17.8 GB`	Click
Q4_K_M	`18.7 GB`	Click
Q5_K_S	`21.3 GB`	Click
Q5_K_M	`21.8 GB`	Click
Q6_K	`25.2 GB`	Click
Q8_0	`32.6 GB`	Click

🔮 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"

🏆 Credits & Honors

The Open-Source Community: For providing the brilliant base models and fine-tunes that made this synthesis possible.

The BeaverAI Community: The people on the BeaverAI Discord - Without your help I wouldn't do all that.

Mergekit: Once again thank you Arcee AI for the great and easy to use mergekit! And thanks to zerofota and their fork for Gemma 4 support.

Ateron: Big kudos for providing me with the first steps for merging models and your relentless testing and support.

Google Gemini: For once again helping me to craft this specific model card.

Gemma-4-31B-Gembrain-uncensored-heretic

Hourly Usage

Performance Metrics

Model Information

🚨⚠️ I HAVE REACHED HUGGING FACE'S FREE STORAGE LIMIT ⚠️🚨

87% fewer refusals (13/100 Uncensored vs 99/100 Original) while preserving model quality (0.0186 KL divergence).

❤️ Support My Work

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

Targeted components

Performance

MMLU test results:

GGUF Version

💎 GEMBRAIN-31B 🧠

🧠 About The Model

🎚️ Samplers

🫟 GGUF Quants

🔮 Prompt Format

🧪 Merge Details

Phase 01: breadcrumbs_ties

Phase 02: slerp

Phase 03: della_linear

Phase 04: model_stock

Phase 05: arcee_fusion

🏆 Credits & Honors