91% fewer refusals (9/100 Uncensored vs 97/100 Original) while preserving model quality (0.0047 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 virtuous7373/Gemma-4-Harmonia-31B, made using Heretic v1.2.0 with the Arbitrary-Rank Ablation (ARA) method
Abliteration parameters
| Parameter | Value |
|---|---|
| start_layer_index | 14 |
| end_layer_index | 55 |
| preserve_good_behavior_weight | 0.7754 |
| steer_bad_behavior_weight | 0.0001 |
| overcorrect_relative_weight | 0.9765 |
| neighbor_count | 14 |
Targeted components
- attn.o_proj
Performance
| Metric | This model | Original model (Gemma-4-Harmonia-31B) |
|---|---|---|
| KL divergence | 0.0047 | 0 (by definition) |
| Refusals | ✅ 9/100 | ❌ 97/100 |
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: 6014
-
Accuracy: 0.8566 (85.66%)
-
Parse failures: 22
============================================================
Tested subject scores:
- professional_law: 0.7592 (596/785)
- moral_scenarios: 0.8394 (371/442)
- miscellaneous: 0.9243 (354/383)
- professional_psychology: 0.8797 (278/316)
- high_school_psychology: 0.9593 (259/270)
- high_school_macroeconomics: 0.9137 (180/197)
- elementary_mathematics: 0.9239 (170/184)
- moral_disputes: 0.8678 (151/174)
- prehistory: 0.9128 (157/172)
- philosophy: 0.8553 (136/159)
- high_school_biology: 0.9605 (146/152)
- professional_accounting: 0.7902 (113/143)
- clinical_knowledge: 0.8929 (125/140)
- high_school_microeconomics: 0.9632 (131/136)
- nutrition: 0.8815 (119/135)
- professional_medicine: 0.9104 (122/134)
- conceptual_physics: 0.9062 (116/128)
- high_school_mathematics: 0.5669 (72/127)
- human_aging: 0.8448 (98/116)
- security_studies: 0.8571 (96/112)
- high_school_statistics: 0.8649 (96/111)
- marketing: 0.9725 (106/109)
- high_school_world_history: 0.9528 (101/106)
- sociology: 0.9223 (95/103)
- high_school_government_and_politics: 0.9406 (95/101)
- high_school_geography: 0.9596 (95/99)
- high_school_chemistry: 0.7835 (76/97)
- high_school_us_history: 0.9053 (86/95)
- virology: 0.5056 (45/89)
- college_medicine: 0.8636 (76/88)
- world_religions: 0.9205 (81/88)
- high_school_physics: 0.7619 (64/84)
- electrical_engineering: 0.8395 (68/81)
- astronomy: 0.9241 (73/79)
- logical_fallacies: 0.8816 (67/76)
- high_school_european_history: 0.8904 (65/73)
- anatomy: 0.8732 (62/71)
- college_biology: 0.9844 (63/64)
- human_sexuality: 0.8750 (56/64)
- formal_logic: 0.7031 (45/64)
- public_relations: 0.7213 (44/61)
- international_law: 0.8667 (52/60)
- college_physics: 0.7193 (41/57)
- college_mathematics: 0.7818 (43/55)
- econometrics: 0.7407 (40/54)
- jurisprudence: 0.8302 (44/53)
- high_school_computer_science: 0.9808 (51/52)
- machine_learning: 0.8462 (44/52)
- medical_genetics: 0.9020 (46/51)
- global_facts: 0.5686 (29/51)
- management: 0.8800 (44/50)
- us_foreign_policy: 0.9800 (49/50)
- college_chemistry: 0.6170 (29/47)
- abstract_algebra: 0.7447 (35/47)
- business_ethics: 0.8478 (39/46)
- college_computer_science: 0.9333 (42/45)
- computer_security: 0.8605 (37/43)
Heretic:
============================================================
-
Total questions: 7021
-
Correct: 5936
-
Accuracy: 0.8455 (84.55%)
-
Parse failures: 17
============================================================
Tested subject scores:
- professional_law: 0.7121 (559/785)
- moral_scenarios: 0.8281 (366/442)
- miscellaneous: 0.9191 (352/383)
- professional_psychology: 0.8703 (275/316)
- high_school_psychology: 0.9593 (259/270)
- high_school_macroeconomics: 0.9188 (181/197)
- elementary_mathematics: 0.9348 (172/184)
- moral_disputes: 0.8448 (147/174)
- prehistory: 0.9128 (157/172)
- philosophy: 0.8113 (129/159)
- high_school_biology: 0.9605 (146/152)
- professional_accounting: 0.7902 (113/143)
- clinical_knowledge: 0.8786 (123/140)
- high_school_microeconomics: 0.9559 (130/136)
- nutrition: 0.8815 (119/135)
- professional_medicine: 0.9030 (121/134)
- conceptual_physics: 0.8828 (113/128)
- high_school_mathematics: 0.5433 (69/127)
- human_aging: 0.8448 (98/116)
- security_studies: 0.8571 (96/112)
- high_school_statistics: 0.8559 (95/111)
- marketing: 0.9817 (107/109)
- high_school_world_history: 0.9528 (101/106)
- sociology: 0.9223 (95/103)
- high_school_government_and_politics: 0.9406 (95/101)
- high_school_geography: 0.9596 (95/99)
- high_school_chemistry: 0.7835 (76/97)
- high_school_us_history: 0.8947 (85/95)
- virology: 0.5056 (45/89)
- college_medicine: 0.8295 (73/88)
- world_religions: 0.9205 (81/88)
- high_school_physics: 0.7619 (64/84)
- electrical_engineering: 0.8148 (66/81)
- astronomy: 0.9367 (74/79)
- logical_fallacies: 0.8947 (68/76)
- high_school_european_history: 0.8630 (63/73)
- anatomy: 0.8873 (63/71)
- college_biology: 0.9844 (63/64)
- human_sexuality: 0.8750 (56/64)
- formal_logic: 0.7031 (45/64)
- public_relations: 0.6885 (42/61)
- international_law: 0.8667 (52/60)
- college_physics: 0.7193 (41/57)
- college_mathematics: 0.7455 (41/55)
- econometrics: 0.7407 (40/54)
- jurisprudence: 0.8113 (43/53)
- high_school_computer_science: 0.9808 (51/52)
- machine_learning: 0.8077 (42/52)
- medical_genetics: 0.9020 (46/51)
- global_facts: 0.5686 (29/51)
- management: 0.8800 (44/50)
- us_foreign_policy: 0.9600 (48/50)
- college_chemistry: 0.6383 (30/47)
- abstract_algebra: 0.7447 (35/47)
- business_ethics: 0.8478 (39/46)
- college_computer_science: 0.9333 (42/45)
- 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-Harmonia-31B-it-uncensored-heretic-GGUF.
name: clever-basename
merge_method: nullspace
base_model: ./gemma-4-31B-base
models:
- model: ./Gemma4-GarnetV2-31B
parameters:
weight: 1.0
parameters:
protect_base: true
nr: 256
tokenizer:
source: base
chat_template: auto
dtype: float32
out_dtype: bfloat16
---
name: clever-intname
merge_method: cabs
base_model: ./clever-basename
models:
- model: ./clever-basename
- model: ./G4-MeroMero-31B-uncensored-heretic
parameters:
weight: 0.6
n_val: 16
m_val: 32
- model: ./Gemma-4-Gembrain-31B-heretic
parameters:
weight: 0.4
n_val: 11
m_val: 33
default_n_val: 8
default_m_val: 32
pruning_order:
- ./G4-MeroMero-31B-uncensored-heretic
- ./Gemma-4-Gembrain-31B-heretic
dtype: float32
out_dtype: bfloat16
tokenizer:
source: union
chat_template: auto
---
name: Harmonia
merge_method: actmat
base_model: ./gemma-4-Ortenzya-The-Creative-Wordsmith-31B-it-uncensored-heretic
models:
- model: ./gemma-4-Ortenzya-The-Creative-Wordsmith-31B-it-uncensored-heretic
- model: ./LatitudeGames-Equinox-31B
parameters:
weight: 1
- model: ./clever-intname
parameters:
weight: 1
- model: ./Fabled-Gemma4-31B
parameters:
weight: 1
parameters:
epsilon: 1e-6
tokenizer:
source: "union"
dtype: bfloat16
out_dtype: bfloat16
chat_template: auto