Instructions to use llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF",
	filename="G4-MeroMero-26B-A4B-it-uncensored-heretic-BF16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF:Q4_K_M

Use Docker

docker model run hf.co/llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF with Ollama:
```
ollama run hf.co/llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF:Q4_K_M
```

Unsloth Studio

How to use llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF to start chatting

How to use llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF with Docker Model Runner:
```
docker model run hf.co/llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF:Q4_K_M
```

Lemonade

How to use llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF-Q4_K_M

List all available models

lemonade list

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

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.

🎉 Patreon (Monthly) | ☕ Ko-fi (One-time)

Every contribution goes directly toward Hugging Face storage fees to keep models free for everyone.

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:

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 zerofata/G4-MeroMero-26B-A4B, made using Heretic v1.2.0 with the Arbitrary-Rank Ablation (ARA) method

Abliteration parameters

Parameter	Value
start_layer_index	15
end_layer_index	26
preserve_good_behavior_weight	0.3274
steer_bad_behavior_weight	0.0005
overcorrect_relative_weight	0.6647
neighbor_count	15

Targeted components

attn.o_proj

Performance

Metric	This model	Original model (G4-MeroMero-26B-A4B)
KL divergence	0.0152	0 (by definition)
Refusals	✅ 12/100	❌ 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: 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.

Filename	Quant	Description
G4-MeroMero-26B-A4B-it-uncensored-heretic-BF16.gguf	BF16	Full precision
G4-MeroMero-26B-A4B-it-uncensored-heretic-Q8_0.gguf	Q8_0	Near-lossless, recommended
G4-MeroMero-26B-A4B-it-uncensored-heretic-Q6_K.gguf	Q6_K	Excellent quality
G4-MeroMero-26B-A4B-it-uncensored-heretic-Q5_K_M.gguf	Q5_K_M	Good balance
G4-MeroMero-26B-A4B-it-uncensored-heretic-Q5_K_S.gguf	Q5_K_S	Smaller Q5
G4-MeroMero-26B-A4B-it-uncensored-heretic-Q4_K_M.gguf	Q4_K_M	Good for limited VRAM
G4-MeroMero-26B-A4B-it-uncensored-heretic-Q4_K_S.gguf	Q4_K_S	Smaller Q4
G4-MeroMero-26B-A4B-it-uncensored-heretic-Q3_K_L.gguf	Q3_K_L	Low VRAM, decent quality
G4-MeroMero-26B-A4B-it-uncensored-heretic-Q3_K_M.gguf	Q3_K_M	Low VRAM, smaller

Vision Projector

Filename	Quant	Description
G4-MeroMero-26B-A4B-it-uncensored-heretic-mmproj-BF16.gguf	BF16	Native precision

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.

Stardom

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

Downloads last month: 15,217

GGUF

Model size

25B params

Architecture

gemma4

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

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Model tree for llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF

Base model

google/gemma-4-26B-A4B

Finetuned

google/gemma-4-26B-A4B-it

Finetuned

zerofata/G4-MeroMero-26B-A4B

Finetuned

llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic

Quantized

(5)

this model

llmfan46
/

G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF

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

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

❤️ Support My Work

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

Performance

MMLU test results:

Quantizations

Vision Projector

Usage

Mero Mero

Model tree for llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF

Datasets used to train llmfan46/G4-MeroMero-26B-A4B-it-uncensored-heretic-GGUF