Instructions to use llmfan46/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llmfan46/Gemma-4-Gembrain-31B-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/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF",
	filename="Gemma-4-Gembrain-31B-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/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf llmfan46/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf llmfan46/Gemma-4-Gembrain-31B-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 serve -hf llmfan46/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf llmfan46/Gemma-4-Gembrain-31B-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/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf llmfan46/Gemma-4-Gembrain-31B-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/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf llmfan46/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF:Q4_K_M

Use Docker

docker model run hf.co/llmfan46/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use llmfan46/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF with Ollama:
```
ollama run hf.co/llmfan46/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF:Q4_K_M
```

Unsloth Studio

How to use llmfan46/Gemma-4-Gembrain-31B-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/Gemma-4-Gembrain-31B-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/Gemma-4-Gembrain-31B-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/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF to start chatting

How to use llmfan46/Gemma-4-Gembrain-31B-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 serve -hf llmfan46/Gemma-4-Gembrain-31B-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/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use llmfan46/Gemma-4-Gembrain-31B-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 serve -hf llmfan46/Gemma-4-Gembrain-31B-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/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use llmfan46/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf llmfan46/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "llmfan46/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use llmfan46/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF with Docker Model Runner:
```
docker model run hf.co/llmfan46/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF:Q4_K_M
```

Lemonade

How to use llmfan46/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull llmfan46/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-4-Gembrain-31B-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.

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

GGUF quantizations of llmfan46/Gemma-4-Gembrain-31B-it-uncensored-heretic.

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

Parameter	Value
start_layer_index	5
end_layer_index	60
preserve_good_behavior_weight	0.9776
steer_bad_behavior_weight	0.0002
overcorrect_relative_weight	1.0158
neighbor_count	15

Targeted components

attn.o_proj

Performance

Metric	This model	Original model (Gemma-4-Gembrain-31B)
KL divergence	0.0186	0 (by definition)
Refusals	✅ 13/100	❌ 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: 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.).

Quantizations

For the K-quants below, small SSM tensors are kept at higher precision where useful.

-Q6_K keeps ssm_alpha, ssm_beta, and ssm_out as Q8_0.

-Q5_K, Q4_K, and Q3_K quants keep ssm_alpha and ssm_beta as Q8_0, while ssm_out is kept as Q6_K.

This helps preserve the hybrid/SSM blocks with a small file-size increase.

Filename	Quant	Description
Gemma-4-Gembrain-31B-it-uncensored-heretic-BF16.gguf	BF16	Full precision
Gemma-4-Gembrain-31B-it-uncensored-heretic-Q8_0.gguf	Q8_0	Near-lossless, recommended
Gemma-4-Gembrain-31B-it-uncensored-heretic-Q6_K.gguf	Q6_K	Excellent quality
Gemma-4-Gembrain-31B-it-uncensored-heretic-Q5_K_M.gguf	Q5_K_M	Good balance
Gemma-4-Gembrain-31B-it-uncensored-heretic-Q5_K_S.gguf	Q5_K_S	Smaller Q5
Gemma-4-Gembrain-31B-it-uncensored-heretic-Q4_K_M.gguf	Q4_K_M	Good for limited VRAM
Gemma-4-Gembrain-31B-it-uncensored-heretic-Q4_K_S.gguf	Q4_K_S	Smaller Q4
Gemma-4-Gembrain-31B-it-uncensored-heretic-Q3_K_L.gguf	Q3_K_L	Low VRAM, decent quality
Gemma-4-Gembrain-31B-it-uncensored-heretic-Q3_K_M.gguf	Q3_K_M	Low VRAM, smaller

Vision Projector

Filename	Quant	Description
Gemma-4-Gembrain-31B-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.

💎 GEMBRAIN-31B 🧠

INSANE IN THE GEMBRAIN

ADHERENCEIMPROVED

SWIPE VARIETYINCREASED

CREATIVE PROSEPRESERVED

🧠 About The Model

Gembrain-31B is a synthesis of several models, including Gemsicle-31B as important ingredient. The goal of this release was to stabilize and improve the initial Gemsicle-31B, but also to enhance its logical and lateral thinking, both with and without reasoning.

It's build to create the most unhinged narratives and construct image prompts about anything accordingly to a given structure with high precision.

Expect creative swipe variance, unique and non-robotic prose, and sharper instruction adherence.

🎚️ Samplers

Temperature	1.0
Top-K	0
Top-P	0.95
Min-P	0.03
DRY Multiplier	0.8
DRY Base	1.75
DRY Allowed Length	10
Optional: Adaptive-P Target	0.6
Optional: Adaptive-P Decay	0.5

🫟 GGUF Quants

Quant	Size	Download Link
Q4_K_S	`17.8 GB`	Click
Q4_K_M	`18.7 GB`	Click
Q5_K_S	`21.3 GB`	Click
Q5_K_M	`21.8 GB`	Click
Q6_K	`25.2 GB`	Click
Q8_0	`32.6 GB`	Click

🔮 Prompt Format

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

🧪 Merge Details

This model was systematically created through a five-stage process of priming models for their given purpose and merging the results:

Phase 01: breadcrumbs_ties

Gemopus X MeroMero

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

Phase 02: slerp

GarnetV2 X Musica-v1

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

Phase 03: della_linear

Gemsicle X Gemma-4-31B-it-heretic-ara

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

Phase 04: model_stock

Phase 01 X Phase 02 X Phase 03

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

Phase 05: arcee_fusion

Gemsicle X Phase 04

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

🏆 Credits & Honors

The Open-Source Community: For providing the brilliant base models and fine-tunes that made this synthesis possible.

The BeaverAI Community: The people on the BeaverAI Discord - Without your help I wouldn't do all that.

Mergekit: Once again thank you Arcee AI for the great and easy to use mergekit! And thanks to zerofota and their fork for Gemma 4 support.

Ateron: Big kudos for providing me with the first steps for merging models and your relentless testing and support.

Google Gemini: For once again helping me to craft this specific model card.

Downloads last month: 2,153

GGUF

Model size

31B params

Architecture

gemma4

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for llmfan46/Gemma-4-Gembrain-31B-it-uncensored-heretic-GGUF

Base model

Nimbz/Gemma-4-Gembrain-31B

Finetuned

llmfan46/Gemma-4-Gembrain-31B-it-uncensored-heretic

Quantized

(4)

this model