license: apache-2.0
datasets:

• zerofata/Instruct-Anime
• zerofata/Gemini-3.1-Pro-SmallWiki
• zerofata/Gemini-3.1-Pro-GLM5-Characters
• zerofata/Roleplay-Anime-Characters
  base_model:
• llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic
  tags:
• heretic
• uncensored
• decensored
• abliterated
  thumbnail: "https://cdn-uploads.huggingface.co/production/uploads/65b19c6c638328850e12d38c/xBv_weuMs5x3i4WFDRksn.png"

GGUF quantizations of llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic.

88% fewer refusals (12/100 Uncensored vs 99/100 Original) while preserving model quality (0.0152 KL divergence).

❤️ Support My Work

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

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/G4-MeroMero-26B-A4B, 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: 5758

• Accuracy: 0.8201 (82.01%)

• Parse failures: 9

============================================================

Tested subject scores:

• professional_law: 0.6841 (537/785)
• moral_scenarios: 0.6991 (309/442)
• miscellaneous: 0.9191 (352/383)
• professional_psychology: 0.8829 (279/316)
• high_school_psychology: 0.9556 (258/270)
• high_school_macroeconomics: 0.8934 (176/197)
• elementary_mathematics: 0.8804 (162/184)
• moral_disputes: 0.8333 (145/174)
• prehistory: 0.9070 (156/172)
• philosophy: 0.8365 (133/159)
• high_school_biology: 0.9605 (146/152)
• professional_accounting: 0.7692 (110/143)
• clinical_knowledge: 0.8714 (122/140)
• high_school_microeconomics: 0.9265 (126/136)
• nutrition: 0.8815 (119/135)
• professional_medicine: 0.8433 (113/134)
• conceptual_physics: 0.8672 (111/128)
• high_school_mathematics: 0.4803 (61/127)
• human_aging: 0.7931 (92/116)
• security_studies: 0.7946 (89/112)
• high_school_statistics: 0.8018 (89/111)
• marketing: 0.9725 (106/109)
• high_school_world_history: 0.8962 (95/106)
• sociology: 0.9029 (93/103)
• high_school_government_and_politics: 0.9505 (96/101)
• high_school_geography: 0.9394 (93/99)
• high_school_chemistry: 0.8144 (79/97)
• high_school_us_history: 0.9158 (87/95)
• virology: 0.5393 (48/89)
• college_medicine: 0.8068 (71/88)
• world_religions: 0.8636 (76/88)
• high_school_physics: 0.7024 (59/84)
• electrical_engineering: 0.7901 (64/81)
• astronomy: 0.9114 (72/79)
• logical_fallacies: 0.8158 (62/76)
• high_school_european_history: 0.9041 (66/73)
• anatomy: 0.8451 (60/71)
• college_biology: 0.9219 (59/64)
• human_sexuality: 0.8594 (55/64)
• formal_logic: 0.6875 (44/64)
• public_relations: 0.7049 (43/61)
• international_law: 0.9333 (56/60)
• college_physics: 0.7544 (43/57)
• college_mathematics: 0.6182 (34/55)
• econometrics: 0.7407 (40/54)
• jurisprudence: 0.8679 (46/53)
• high_school_computer_science: 0.9423 (49/52)
• machine_learning: 0.8462 (44/52)
• medical_genetics: 0.9216 (47/51)
• global_facts: 0.5294 (27/51)
• management: 0.9000 (45/50)
• us_foreign_policy: 0.9400 (47/50)
• college_chemistry: 0.5532 (26/47)
• abstract_algebra: 0.7234 (34/47)
• business_ethics: 0.7826 (36/46)
• college_computer_science: 0.8000 (36/45)
• computer_security: 0.8140 (35/43)

Heretic:

============================================================

• Total questions: 7021

• Correct: 5698

• Accuracy: 0.8116 (81.16%)

• Parse failures: 6

============================================================

Tested subject scores:

• professional_law: 0.6510 (511/785)
• moral_scenarios: 0.7059 (312/442)
• miscellaneous: 0.9164 (351/383)
• professional_psychology: 0.8861 (280/316)
• high_school_psychology: 0.9519 (257/270)
• high_school_macroeconomics: 0.8985 (177/197)
• elementary_mathematics: 0.8696 (160/184)
• moral_disputes: 0.8276 (144/174)
• prehistory: 0.8953 (154/172)
• philosophy: 0.8428 (134/159)
• high_school_biology: 0.9539 (145/152)
• professional_accounting: 0.6853 (98/143)
• clinical_knowledge: 0.9000 (126/140)
• high_school_microeconomics: 0.9265 (126/136)
• nutrition: 0.8815 (119/135)
• professional_medicine: 0.8134 (109/134)
• conceptual_physics: 0.8516 (109/128)
• high_school_mathematics: 0.4803 (61/127)
• human_aging: 0.8276 (96/116)
• security_studies: 0.7946 (89/112)
• high_school_statistics: 0.7658 (85/111)
• marketing: 0.9725 (106/109)
• high_school_world_history: 0.8868 (94/106)
• sociology: 0.8932 (92/103)
• high_school_government_and_politics: 0.9505 (96/101)
• high_school_geography: 0.9394 (93/99)
• high_school_chemistry: 0.7526 (73/97)
• high_school_us_history: 0.9158 (87/95)
• virology: 0.5169 (46/89)
• college_medicine: 0.8409 (74/88)
• world_religions: 0.8750 (77/88)
• high_school_physics: 0.6786 (57/84)
• electrical_engineering: 0.8025 (65/81)
• astronomy: 0.9114 (72/79)
• logical_fallacies: 0.7763 (59/76)
• high_school_european_history: 0.8904 (65/73)
• anatomy: 0.8732 (62/71)
• college_biology: 0.8906 (57/64)
• human_sexuality: 0.9219 (59/64)
• formal_logic: 0.6875 (44/64)
• public_relations: 0.7213 (44/61)
• international_law: 0.9333 (56/60)
• college_physics: 0.6842 (39/57)
• college_mathematics: 0.5636 (31/55)
• econometrics: 0.7222 (39/54)
• jurisprudence: 0.8491 (45/53)
• high_school_computer_science: 0.9423 (49/52)
• machine_learning: 0.8077 (42/52)
• medical_genetics: 0.9216 (47/51)
• global_facts: 0.4706 (24/51)
• management: 0.8800 (44/50)
• us_foreign_policy: 0.9400 (47/50)
• college_chemistry: 0.4894 (23/47)
• abstract_algebra: 0.7447 (35/47)
• business_ethics: 0.8261 (38/46)
• college_computer_science: 0.8222 (37/45)
• computer_security: 0.8605 (37/43)

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

Quantizations

For the K-quants below, selected Gemma 4 attention and FFN tensors are kept at higher precision where useful.

Gemma 4 does not use the ssm_alpha, ssm_beta, or ssm_out tensors found in some Qwen-style hybrid/SSM architectures. Instead, these GGUFs preserve key Gemma 4 attention projection tensors at higher precision.

• Q6_K uses a higher-quality XL-style layout:

  • attn_q, attn_k, attn_v, and attn_output are kept as Q8_0.
  • ffn_gate, ffn_up, and ffn_down are kept as Q8_0.
  • ffn_down_exps is requested as Q6_K where supported. Some tensors may fall back to Q8_0 due to Gemma 4 tensor shape constraints.

• Q5_K_M, Q5_K_S, Q4_K_M, and Q4_K_S keep the main attention projection tensors as Q8_0:

  • attn_q
  • attn_k
  • attn_v
  • attn_output

• Q3_K_L and Q3_K_M keep the main attention projection tensors as BF16:

  • attn_q
  • attn_k
  • attn_v
  • attn_output

This helps preserve Gemma 4’s attention path at higher precision, especially for lower-bit quants, while avoiding large file-size increases from unnecessarily up-quantizing the largest MoE expert tensors.

Vision Projector

A Vision Projector File is Required for vision/multimodal capabilities. Use alongside any quantization above.

Usage

Works with llama.cpp, LM Studio, Ollama, and other GGUF-compatible tools.

Mero Mero

Gemma4 26B A4B

01 Overview

God, this model was difficult to work with.

Google cooked, there wasn't a lot to improve but there was a lot to break.

This model is a finetune that was merged back into the original instruct. It feels a lot like the original instruct. However, reasoning is more structured, using less tokens during RP and this model generally has a slightly less verbose / flowery writing style.

Main weakness of this model I think is the swipe variety hasn't improved. Logic and repetition I think are roughly on par with the original.

Supports both thinking and non thinking.

02 SillyTavern Settings

Suggested Roleplay Format

ActionsIn plaintext

Dialogue"In quotes"

Thoughts*In asterisks*

Recommended Samplers

Temp0.8 - 1.0

MinP0.05

Instruct

Gemma 4 - Think

Gemma 4 - NoThink

03 Quantizations

GGUF

iMatrix

04 Creation Process

Creation Process: SFT > Merge

SFT on approx 35 million tokens.

Despite using 35 million tokens, this dataset is fairly modest in size. Trainable is somewhere in the rough ballpark of 15 million. The extra tokens are from a new multi turn RP dataset that I train last turn only.

Feels like Google left the instruct model at the razor's edge of overfitting. Finetune it at all and it feels like it'll rapidly lose intelligence, despite taking the writing style nicely. Hard to tell if you're overfitting or underfitting.

My solution was to blast the model with my data anyway to ensure it picked up the new reasoning format and writing style and then merge that back into the instruct to heal the logic damage. There's still room for a better merge that keeps more of the writing style and potentially using the base model to undo some of the overfitting.

Trained using Axolotl.

Mergekit Config

models:
  - model: google/gemma-4-26B-A4B-it
    parameters:
      weight: 0.5
  - model: ApocalypseParty/G4-26B-SFT-6
    parameters:
      weight: 0.5
merge_method: linear
dtype: bfloat16

Axolotl Config

# Gemma 4 26B-A4B MoE QLoRA with ScatterMoE kernels
#
# Validated: 50 steps on FineTome-100k, loss 8.8 -> 1.8, single RTX 5090 (32GB)
# torch_compile=true: 21 GiB peak VRAM, ~230 tok/s, 336s total
#
# Key notes:
# - Max sequence length on 32GB GPU: 2048 (micro_batch_size=1, SDP attention).
#   4096 seq_len OOMs due to head_dim=512 math SDP materializing full score matrix.
#   Use 48GB+ GPUs for longer sequences or multi-GPU with FSDP.
 
base_model: google/gemma-4-26B-A4B-it
 
plugins:
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
  - axolotl.integrations.kernels.KernelsPlugin
  - axolotl.integrations.liger.LigerPlugin
use_kernels: true
use_scattermoe: true
cut_cross_entropy: true
experts_implementation: scattermoe
liger_layer_norm: true
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_rms_norm_gated: true
strict: false
 
datasets:
  - path: ./data/gemma_4_sft_5_masked_20260415_082234.jsonl
val_set_size: 0.02
output_dir: ./G4-26B-SFT-6
 
sequence_len: 10756
pad_to_sequence_len: true
sample_packing: true
 
load_in_4bit: false
#quantize_moe_experts: true
adapter: lora
lora_r: 128
lora_alpha: 128
peft_use_rslora: true
lora_dropout: 0.0
freeze_mm_modules: true
 
# Restrict LoRA to text backbone only (skip vision/audio encoders)
# using regex to match only the text decoder attention projections.
lora_target_modules: 'model.language_model.layers.[\d]+.(_checkpoint_wrapped_module.)?(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
 
# MoE expert LoRA (3D Parameter tensors, not nn.Linear)
lora_target_parameters:
  - experts.gate_up_proj
  - experts.down_proj
 
lora_mlp_kernel: false
lora_qkv_kernel: false
lora_o_kernel: false
 
#bnb_config_kwargs:
#  bnb_4bit_use_double_quant: true
 
wandb_project: G4-26B-SFT
wandb_name: G4-26B-SFT-6
 
gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 2
optimizer: adamw_torch_fused
lr_scheduler: constant_with_warmup
learning_rate: 1e-5
max_grad_norm: 1.0
 
bf16: auto
tf32: true
 
#gradient_checkpointing: true
#activation_offloading: true
logging_steps: 1
 
# FA2 not supported
sdp_attention: true
#flex_attention: true
#torch_compile: true
flash_attention: false
 
warmup_ratio: 0.1
evals_per_epoch: 4
saves_per_epoch: 4
weight_decay: 0.01
special_tokens:
 
fsdp_config:
  fsdp_version: 2
  offload_params: false
  cpu_ram_efficient_loading: false
  auto_wrap_policy: TRANSFORMER_BASED_WRAP
  transformer_layer_cls_to_wrap: Gemma4TextDecoderLayer
  state_dict_type: FULL_STATE_DICT
  sharding_strategy: FULL_SHARD
  reshard_after_forward: true
  activation_checkpointing: true