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

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

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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

• attn.o_proj

Performance

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.

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

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

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

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

models:
  - model: ./phase01_breadcrumbs_ties
  - model: ./phase02_slerp
merge_method: model_stock
base_model: ./phase03_della_linear
dtype: bfloat16
tokenizer_source: "base"

models:
  - model: ./Gemsicle-31B
  - model: ./phase04_model_stock
merge_method: arcee_fusion
base_model: ./Gemsicle-31B
dtype: bfloat16
tokenizer_source: "base"