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97% fewer refusals (4/100 Uncensored vs 90/100 Original) while preserving model quality (0.0195 KL divergence).

❤️ Support My Work

Creating these models takes significant time, work and compute. If you find them useful consider supporting me:

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🎉 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 zerofata/MS3.2-PaintedFantasy-Visage-v3-34B, made using Heretic v1.2.0 with the Arbitrary-Rank Ablation (ARA) method

Abliteration parameters

Parameter	Value
start_layer_index	3
end_layer_index	29
preserve_good_behavior_weight	0.8481
steer_bad_behavior_weight	0.0002
overcorrect_relative_weight	0.8911
neighbor_count	5

Targeted components

attn.o_proj

Performance

Metric	This model	Original model (MS3.2-PaintedFantasy-Visage-v3-34B)
KL divergence	0.0195	0 (by definition)
Refusals	✅ 4/100	❌ 90/100

PIQA test results with batch size 128:

Original:

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
piqa	1	none	0	acc	↑	0.8210	±	0.0089
		none	0	acc_norm	↑	0.8313	±	0.0087

Heretic:

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
piqa	1	none	0	acc	↑	0.8210	±	0.0089
		none	0	acc_norm	↑	0.8324	±	0.0087

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. PIQA (Physical Intuition Question Answering) a ~1,800 questions tests common-sense understanding of how the physical world works with benchmark scores to measure physical reasoning ability. The Heretic model's acc and acc_norm scores closer to the original model's indicate better capability preservation, a big decrease in acc and acc_norm in the Heretic model compared to Original model's results means a big decrease in the Hereticated model capabilities. acc measures raw accuracy (which answer gets higher probability), while acc_norm measures length-normalized accuracy (corrects for answer length bias). For this purpose, acc_norm matters more because longer answers naturally have lower probabilities (more tokens = more chances to lose probability). Without normalization, models favor shorter answers unfairly. acc_norm divides by answer length to correct this.

MMLU test results with batch size 16:

Original:

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
mmlu	2	none		acc	↑	0.7763	±	0.0033
- humanities	2	none		acc	↑	0.6948	±	0.0063
- formal_logic	1	none	0	acc	↑	0.5397	±	0.0446
- high_school_european_history	1	none	0	acc	↑	0.8485	±	0.0280
- high_school_us_history	1	none	0	acc	↑	0.9510	±	0.0152
- high_school_world_history	1	none	0	acc	↑	0.9030	±	0.0193
- international_law	1	none	0	acc	↑	0.8926	±	0.0283
- jurisprudence	1	none	0	acc	↑	0.8241	±	0.0368
- logical_fallacies	1	none	0	acc	↑	0.8466	±	0.0283
- moral_disputes	1	none	0	acc	↑	0.8092	±	0.0212
- moral_scenarios	1	none	0	acc	↑	0.4782	±	0.0167
- philosophy	1	none	0	acc	↑	0.8360	±	0.0210
- prehistory	1	none	0	acc	↑	0.8765	±	0.0183
- professional_law	1	none	0	acc	↑	0.5984	±	0.0125
- world_religions	1	none	0	acc	↑	0.8655	±	0.0262
- other	2	none		acc	↑	0.8252	±	0.0065
- business_ethics	1	none	0	acc	↑	0.8100	±	0.0394
- clinical_knowledge	1	none	0	acc	↑	0.8226	±	0.0235
- college_medicine	1	none	0	acc	↑	0.7803	±	0.0316
- global_facts	1	none	0	acc	↑	0.6000	±	0.0492
- human_aging	1	none	0	acc	↑	0.8072	±	0.0265
- management	1	none	0	acc	↑	0.9029	±	0.0293
- marketing	1	none	0	acc	↑	0.9444	±	0.0150
- medical_genetics	1	none	0	acc	↑	0.9000	±	0.0302
- miscellaneous	1	none	0	acc	↑	0.9119	±	0.0101
- nutrition	1	none	0	acc	↑	0.8562	±	0.0201
- professional_accounting	1	none	0	acc	↑	0.6383	±	0.0287
- professional_medicine	1	none	0	acc	↑	0.8603	±	0.0211
- virology	1	none	0	acc	↑	0.5783	±	0.0384
- social sciences	2	none		acc	↑	0.8739	±	0.0059
- econometrics	1	none	0	acc	↑	0.6667	±	0.0443
- high_school_geography	1	none	0	acc	↑	0.9242	±	0.0189
- high_school_government_and_politics	1	none	0	acc	↑	0.9689	±	0.0125
- high_school_macroeconomics	1	none	0	acc	↑	0.8231	±	0.0193
- high_school_microeconomics	1	none	0	acc	↑	0.9160	±	0.0180
- high_school_psychology	1	none	0	acc	↑	0.9413	±	0.0101
- human_sexuality	1	none	0	acc	↑	0.8702	±	0.0295
- professional_psychology	1	none	0	acc	↑	0.8513	±	0.0144
- public_relations	1	none	0	acc	↑	0.8091	±	0.0376
- security_studies	1	none	0	acc	↑	0.8041	±	0.0254
- sociology	1	none	0	acc	↑	0.8905	±	0.0221
- us_foreign_policy	1	none	0	acc	↑	0.9100	±	0.0288
- stem	2	none		acc	↑	0.7545	±	0.0073
- abstract_algebra	1	none	0	acc	↑	0.5600	±	0.0499
- anatomy	1	none	0	acc	↑	0.8519	±	0.0307
- astronomy	1	none	0	acc	↑	0.9079	±	0.0235
- college_biology	1	none	0	acc	↑	0.9306	±	0.0213
- college_chemistry	1	none	0	acc	↑	0.4900	±	0.0502
- college_computer_science	1	none	0	acc	↑	0.6800	±	0.0469
- college_mathematics	1	none	0	acc	↑	0.5200	±	0.0502
- college_physics	1	none	0	acc	↑	0.5784	±	0.0491
- computer_security	1	none	0	acc	↑	0.8400	±	0.0368
- conceptual_physics	1	none	0	acc	↑	0.8426	±	0.0238
- electrical_engineering	1	none	0	acc	↑	0.7793	±	0.0346
- elementary_mathematics	1	none	0	acc	↑	0.7804	±	0.0213
- high_school_biology	1	none	0	acc	↑	0.9226	±	0.0152
- high_school_chemistry	1	none	0	acc	↑	0.7241	±	0.0314
- high_school_computer_science	1	none	0	acc	↑	0.8800	±	0.0327
- high_school_mathematics	1	none	0	acc	↑	0.5815	±	0.0301
- high_school_physics	1	none	0	acc	↑	0.6689	±	0.0384
- high_school_statistics	1	none	0	acc	↑	0.7361	±	0.0301
- machine_learning	1	none	0	acc	↑	0.7143	±	0.0429

Groups	Version	Filter	Metric		Value		Stderr
mmlu	2	none	acc	↑	0.7763	±	0.0033
- humanities	2	none	acc	↑	0.6948	±	0.0063
- other	2	none	acc	↑	0.8252	±	0.0065
- social sciences	2	none	acc	↑	0.8739	±	0.0059
- stem	2	none	acc	↑	0.7545	±	0.0073

Heretic:

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
mmlu	2	none		acc	↑	0.7711	±	0.0033
- humanities	2	none		acc	↑	0.6869	±	0.0063
- formal_logic	1	none	0	acc	↑	0.5317	±	0.0446
- high_school_european_history	1	none	0	acc	↑	0.8485	±	0.0280
- high_school_us_history	1	none	0	acc	↑	0.9412	±	0.0165
- high_school_world_history	1	none	0	acc	↑	0.9072	±	0.0189
- international_law	1	none	0	acc	↑	0.8760	±	0.0301
- jurisprudence	1	none	0	acc	↑	0.8426	±	0.0352
- logical_fallacies	1	none	0	acc	↑	0.8221	±	0.0300
- moral_disputes	1	none	0	acc	↑	0.8064	±	0.0213
- moral_scenarios	1	none	0	acc	↑	0.4514	±	0.0166
- philosophy	1	none	0	acc	↑	0.8167	±	0.0220
- prehistory	1	none	0	acc	↑	0.8889	±	0.0175
- professional_law	1	none	0	acc	↑	0.5945	±	0.0125
- world_religions	1	none	0	acc	↑	0.8772	±	0.0252
- other	2	none		acc	↑	0.8230	±	0.0066
- business_ethics	1	none	0	acc	↑	0.8000	±	0.0402
- clinical_knowledge	1	none	0	acc	↑	0.8189	±	0.0237
- college_medicine	1	none	0	acc	↑	0.7688	±	0.0321
- global_facts	1	none	0	acc	↑	0.6300	±	0.0485
- human_aging	1	none	0	acc	↑	0.7937	±	0.0272
- management	1	none	0	acc	↑	0.9126	±	0.0280
- marketing	1	none	0	acc	↑	0.9487	±	0.0145
- medical_genetics	1	none	0	acc	↑	0.8900	±	0.0314
- miscellaneous	1	none	0	acc	↑	0.9055	±	0.0105
- nutrition	1	none	0	acc	↑	0.8497	±	0.0205
- professional_accounting	1	none	0	acc	↑	0.6348	±	0.0287
- professional_medicine	1	none	0	acc	↑	0.8713	±	0.0203
- virology	1	none	0	acc	↑	0.5843	±	0.0384
- social sciences	2	none		acc	↑	0.8684	±	0.0060
- econometrics	1	none	0	acc	↑	0.6579	±	0.0446
- high_school_geography	1	none	0	acc	↑	0.9091	±	0.0205
- high_school_government_and_politics	1	none	0	acc	↑	0.9689	±	0.0125
- high_school_macroeconomics	1	none	0	acc	↑	0.8077	±	0.0200
- high_school_microeconomics	1	none	0	acc	↑	0.9034	±	0.0192
- high_school_psychology	1	none	0	acc	↑	0.9431	±	0.0099
- human_sexuality	1	none	0	acc	↑	0.8550	±	0.0309
- professional_psychology	1	none	0	acc	↑	0.8546	±	0.0143
- public_relations	1	none	0	acc	↑	0.7909	±	0.0390
- security_studies	1	none	0	acc	↑	0.7918	±	0.0260
- sociology	1	none	0	acc	↑	0.8905	±	0.0221
- us_foreign_policy	1	none	0	acc	↑	0.9100	±	0.0288
- stem	2	none		acc	↑	0.7507	±	0.0074
- abstract_algebra	1	none	0	acc	↑	0.5700	±	0.0498
- anatomy	1	none	0	acc	↑	0.8296	±	0.0325
- astronomy	1	none	0	acc	↑	0.8947	±	0.0250
- college_biology	1	none	0	acc	↑	0.9167	±	0.0231
- college_chemistry	1	none	0	acc	↑	0.5200	±	0.0502
- college_computer_science	1	none	0	acc	↑	0.6800	±	0.0469
- college_mathematics	1	none	0	acc	↑	0.5500	±	0.0500
- college_physics	1	none	0	acc	↑	0.6176	±	0.0484
- computer_security	1	none	0	acc	↑	0.8100	±	0.0394
- conceptual_physics	1	none	0	acc	↑	0.8426	±	0.0238
- electrical_engineering	1	none	0	acc	↑	0.7793	±	0.0346
- elementary_mathematics	1	none	0	acc	↑	0.7804	±	0.0213
- high_school_biology	1	none	0	acc	↑	0.9161	±	0.0158
- high_school_chemistry	1	none	0	acc	↑	0.6995	±	0.0323
- high_school_computer_science	1	none	0	acc	↑	0.8800	±	0.0327
- high_school_mathematics	1	none	0	acc	↑	0.5926	±	0.0300
- high_school_physics	1	none	0	acc	↑	0.6623	±	0.0386
- high_school_statistics	1	none	0	acc	↑	0.7083	±	0.0310
- machine_learning	1	none	0	acc	↑	0.6964	±	0.0436

Groups	Version	Filter	Metric		Value		Stderr
mmlu	2	none	acc	↑	0.7711	±	0.0033
- humanities	2	none	acc	↑	0.6869	±	0.0063
- other	2	none	acc	↑	0.8230	±	0.0066
- social sciences	2	none	acc	↑	0.8684	±	0.0060
- stem	2	none	acc	↑	0.7507	±	0.0074

MMLU - Massive Multitask Language Understanding, ~14,000 multiple-choice questions across 57 subjects (math, history, law, medicine, etc.).

GGUF Version

GGUF quantizations available here llmfan46/MS3.2-PaintedFantasy-Visage-v3-34B-ultra-uncensored-heretic-GGUF.

PAINTED FANTASY VISAGE v3

Mistral Small 3.2 Upscaled 34B

Overview

No layer left behind edition.

Upscale redone with the missing final layer included. The original upscales were always missing a layer, but I never troubleshooted to identify *what* layer was missing. Turns out it was the final layer. That's kind of an important one.

This model is an uncensored, creative writing and RP model. Compared to the older version, it is smarter and I think has a bit less repetition. The old V2 version though is slightly more creative due to the instability it had.

SillyTavern Settings

Recommended Roleplay Format

> Actions: In plaintext

> Dialogue: "In quotes"

> Thoughts: *In asterisks*

Recommended Samplers

> Temp: 0.7-0.8

> MinP: 0.05 - 0.1

> TopP: 0.95

> Dry: 0.8, 1.75, 4

Instruct

Mistral v7 Tekken

Quantizations

GGUF

> iMatrix (bartowski)

EXL3

> 3bpw

> 4bpw

> 4.25bpw

> 5bpw

> 6bpw

Creation Process

Creation Process: Upscale > CPT > SFT > DPO

Pretrained on approx 300MB of light novel and FineWeb-2 corpus.

SFT on approx 8 million tokens, SFW / NSFW RP, stories and creative instruct data.

DPO on a high quality RP / NSFW dataset with a focus on improving instruction following, reducing repetition and fixing common model mistakes.

Merge configurations used during the model creation process.

Upscale (Passthrough)

base_model: ConicCat/Mistral-Small-3.2-AntiRep-24B
merge_method: passthrough
dtype: bfloat16
slices:
  - sources:
      - model: ConicCat/Mistral-Small-3.2-AntiRep-24B
        layer_range: [0, 29]
  - sources:
      - model: ConicCat/Mistral-Small-3.2-AntiRep-24B
        layer_range: [10, 40]

Not optimized for cost / performance efficiency, YMMV.

Pretrain 4*H100

# ====================
# MODEL CONFIGURATION
# ====================
base_model: ../mergekit/pf_v3_upscale
model_type: MistralForCausalLM
tokenizer_type: AutoTokenizer
chat_template: mistral_v7_tekken
# ====================
# DATASET CONFIGURATION
# ====================
datasets:
  - path: ./data/pretrain_dataset_v5_stripped.jsonl
    type: completion
[object Object]
dataset_prepared_path:
train_on_inputs: false  # Only train on assistant responses
[object Object]
# ====================
# QLORA CONFIGURATION
# ====================
adapter: qlora
load_in_4bit: true
lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true
# lora_modules_to_save:  # Uncomment only if you added NEW tokens
[object Object]
# ====================
# TRAINING PARAMETERS
# ====================
num_epochs: 1
micro_batch_size: 4
gradient_accumulation_steps: 1
learning_rate: 4e-5
optimizer: paged_adamw_8bit
lr_scheduler: rex
warmup_ratio: 0.05
weight_decay: 0.01
max_grad_norm: 1.0
[object Object]
# ====================
# SEQUENCE & PACKING
# ====================
sequence_len: 12288
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
[object Object]
# ====================
# HARDWARE OPTIMIZATIONS
# ====================
bf16: auto
flash_attention: true
gradient_checkpointing: offload
deepspeed: deepspeed_configs/zero1.json
[object Object]
plugins:
  - axolotl.integrations.liger.LigerPlugin
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
cut_cross_entropy: true
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_cross_entropy: false  # Cut Cross Entropy overrides this
liger_fused_linear_cross_entropy: false  # Cut Cross Entropy overrides this
[object Object]
# ====================
# EVALUATION & CHECKPOINTING
# ====================
save_strategy: steps
save_steps: 40
save_total_limit: 5  # Keep best + last few checkpoints
load_best_model_at_end: true
greater_is_better: false
[object Object]
# ====================
# LOGGING & OUTPUT
# ====================
output_dir: ./Visage-V3-PT-1
logging_steps: 2
save_safetensors: true
[object Object]
# ====================
# WANDB TRACKING
# ====================
wandb_project: Visage-V3-PT
# wandb_entity: your_entity
wandb_name: Visage-V3-PT-1

SFT 4*H100

# ====================
# MODEL CONFIGURATION
# ====================
base_model: ./Visage-V3-PT-1/merged
model_type: MistralForCausalLM
tokenizer_type: AutoTokenizer
chat_template: mistral_v7_tekken
[object Object]
# ====================
# DATASET CONFIGURATION
# ====================
datasets:
  - path: ./data/dataset.jsonl
    type: chat_template
    split: train
    chat_template_strategy: tokenizer
    field_messages: messages
    message_property_mappings:
      role: role
      content: content
    roles:
      user: ["user"]
      assistant: ["assistant"]
      system: ["system"]
[object Object]
dataset_prepared_path:
train_on_inputs: false  # Only train on assistant responses
[object Object]
# ====================
# QLORA CONFIGURATION
# ====================
adapter: qlora
load_in_4bit: true
lora_r: 128
lora_alpha: 128
lora_dropout: 0.1
lora_target_linear: true
# lora_modules_to_save:  # Uncomment only if you added NEW tokens
[object Object]
# ====================
# TRAINING PARAMETERS
# ====================
num_epochs: 3
micro_batch_size: 4
gradient_accumulation_steps: 1
learning_rate: 1e-5
optimizer: paged_adamw_8bit
lr_scheduler: rex
warmup_ratio: 0.05
weight_decay: 0.01
max_grad_norm: 1.0
[object Object]
# ====================
# SEQUENCE & PACKING
# ====================
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
[object Object]
# ====================
# HARDWARE OPTIMIZATIONS
# ====================
bf16: auto
flash_attention: true
gradient_checkpointing: offload
deepspeed: deepspeed_configs/zero1.json
[object Object]
plugins:
  - axolotl.integrations.liger.LigerPlugin
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
cut_cross_entropy: true
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_cross_entropy: false  # Cut Cross Entropy overrides this
liger_fused_linear_cross_entropy: false  # Cut Cross Entropy overrides this
[object Object]
# ====================
# EVALUATION & CHECKPOINTING
# ====================
save_strategy: steps
save_steps: 20
save_total_limit: 5  # Keep best + last few checkpoints
load_best_model_at_end: true
metric_for_best_model: eval_loss
greater_is_better: false
[object Object]
# ====================
# LOGGING & OUTPUT
# ====================
output_dir: ./Visage-V3-PT-1-SFT-2
logging_steps: 1
save_safetensors: true
[object Object]
# ====================
# WANDB TRACKING
# ====================
wandb_project: Visage-V3-SFT
# wandb_entity: your_entity
wandb_name: Visage-V3-PT-1-SFT-2

DPO 2*H200

# ====================
# MODEL CONFIGURATION
# ====================
base_model: ./Visage-V3-PT-1-SFT-2/merged
model_type: MistralForCausalLM
tokenizer_type: AutoTokenizer
chat_template: mistral_v7_tekken
[object Object]
# ====================
# RL/DPO CONFIGURATION
# ====================
rl: dpo
rl_beta: 0.085
[object Object]
# ====================
# DATASET CONFIGURATION
# ====================
datasets:
  - path: ./data/handcrafted_dataset_mistral_rep.jsonl
    type: chat_template.default
    field_messages: messages
    field_chosen: chosen
    field_rejected: rejected
    message_property_mappings:
      role: role
      content: content
    roles:
      system: ["system"]
      user: ["user"]
      assistant: ["assistant"]
  - path: ./data/approved_automated_l3_dataset.jsonl
    type: chat_template.default
    field_messages: messages
    field_chosen: chosen
    field_rejected: rejected
    message_property_mappings:
      role: role
      content: content
    roles:
      system: ["system"]
      user: ["user"]
      assistant: ["assistant"]
dataset_prepared_path:
train_on_inputs: false  # Only train on assistant responses
[object Object]
# ====================
# QLORA CONFIGURATION
# ====================
adapter: lora
load_in_8bit: true
lora_r: 16
lora_alpha: 32
lora_dropout: 0.1
lora_target_linear: true
# lora_modules_to_save:  # Uncomment only if you added NEW tokens
[object Object]
# ====================
# TRAINING PARAMETERS
# ====================
num_epochs: 1
micro_batch_size: 2
gradient_accumulation_steps: 4
learning_rate: 2e-6
optimizer: adamw_torch_fused
lr_scheduler: cosine
warmup_steps: 5
weight_decay: 0.01
max_grad_norm: 1.0
[object Object]
# ====================
# SEQUENCE CONFIGURATION
# ====================
sequence_len: 8192
pad_to_sequence_len: true
[object Object]
# ====================
# HARDWARE OPTIMIZATIONS
# ====================
bf16: auto
tf32: false
flash_attention: true
gradient_checkpointing: offload
[object Object]
plugins:
  - axolotl.integrations.liger.LigerPlugin
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
cut_cross_entropy: true
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_cross_entropy: false  # Cut Cross Entropy overrides this
liger_fused_linear_cross_entropy: false  # Cut Cross Entropy overrides this
deepspeed: deepspeed_configs/zero1.json
[object Object]
# ====================
# CHECKPOINTING
# ====================
save_steps: 10
save_total_limit: 10
load_best_model_at_end: true
metric_for_best_model: eval_loss
greater_is_better: false
[object Object]
# ====================
# LOGGING & OUTPUT
# ====================
output_dir: ./Visage-V3-PT-1-SFT-2-DPO-2
logging_steps: 1
save_safetensors: true
[object Object]
# ====================
# WANDB TRACKING
# ====================
wandb_project: Visage-V3-DPO
# wandb_entity: your_entity
wandb_name: Visage-V3-PT-1-SFT-2-DPO-2