Instructions to use saidutta69/gemma-3-4b-it-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saidutta69/gemma-3-4b-it-heretic with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="saidutta69/gemma-3-4b-it-heretic",
	filename="gemma-3-4b-it-heretic-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use saidutta69/gemma-3-4b-it-heretic 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 saidutta69/gemma-3-4b-it-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/gemma-3-4b-it-heretic:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf saidutta69/gemma-3-4b-it-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/gemma-3-4b-it-heretic: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 saidutta69/gemma-3-4b-it-heretic:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf saidutta69/gemma-3-4b-it-heretic: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 saidutta69/gemma-3-4b-it-heretic:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf saidutta69/gemma-3-4b-it-heretic:Q4_K_M

Use Docker

docker model run hf.co/saidutta69/gemma-3-4b-it-heretic:Q4_K_M

LM Studio
Jan

vLLM

How to use saidutta69/gemma-3-4b-it-heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saidutta69/gemma-3-4b-it-heretic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saidutta69/gemma-3-4b-it-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/saidutta69/gemma-3-4b-it-heretic:Q4_K_M

Ollama
How to use saidutta69/gemma-3-4b-it-heretic with Ollama:
```
ollama run hf.co/saidutta69/gemma-3-4b-it-heretic:Q4_K_M
```

Unsloth Studio

How to use saidutta69/gemma-3-4b-it-heretic 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 saidutta69/gemma-3-4b-it-heretic 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 saidutta69/gemma-3-4b-it-heretic to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for saidutta69/gemma-3-4b-it-heretic to start chatting

Atomic Chat new
Docker Model Runner
How to use saidutta69/gemma-3-4b-it-heretic with Docker Model Runner:
```
docker model run hf.co/saidutta69/gemma-3-4b-it-heretic:Q4_K_M
```

Lemonade

How to use saidutta69/gemma-3-4b-it-heretic with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull saidutta69/gemma-3-4b-it-heretic:Q4_K_M

Run and chat with the model

lemonade run user.gemma-3-4b-it-heretic-Q4_K_M

List all available models

lemonade list

gemma-3-4b-it-heretic

A decensored variant of google/gemma-3-4b-it, produced with Heretic v1.4.0 (directional ablation / "abliteration"). Refusal behavior is suppressed via targeted weight edits to the attention output and MLP down-projections rather than fine-tuning, so the base model's knowledge and instruction-following are left largely intact.

Who this is for: developers who want Google's Gemma 3 4B (text + vision) without the refusal guardrails - a small multimodal uncensored model for local agents, image-grounded Q&A, and research on alignment/refusal mechanics. At 4B it runs on consumer hardware and edge devices.

Why abliteration instead of fine-tuning

Fine-tuning a "helpful" persona on top of RLHF'd refusals fights the base model's training and tends to degrade coherence. Abliteration instead finds and edits the specific weight directions responsible for refusal, leaving the rest of the network (and its capabilities) untouched. See the Heretic repo and the original abliteration writeup for the mechanism.

Abliteration parameters

Parameter	Value
`direction_index`	per layer
`attn.o_proj.max_weight`	1.48
`attn.o_proj.max_weight_position`	21.63
`attn.o_proj.min_weight`	1.44
`attn.o_proj.min_weight_distance`	15.72
`mlp.down_proj.max_weight`	1.47
`mlp.down_proj.max_weight_position`	20.95
`mlp.down_proj.min_weight`	0.16
`mlp.down_proj.min_weight_distance`	18.87

Performance

Metric	This model	gemma-3-4b-it (base)
Refusals (out of 100 adversarial prompts)	18/100	98/100
KL divergence from base	0.6974	0 (by definition)

KL divergence of 0.70 is higher than the dense text-only models here - Gemma 3's multimodal refusal direction is broader, so the edit is wider. Refusals dropped from 98 to 18 out of 100 adversarial prompts. Capability retention is good but not as surgical as the Qwen/Mistral runs.

Made with ❤️ by RACER IS OP - follow for more uncensored models

Files

Safetensors (BF16)

The full-precision weights are in model-0000N-of-0000N.safetensors (see the repo file listing for the exact shard count and sizes).

GGUF quantizations

GGUF quantizations are published for this model (Q4_K_M, Q5_K_M). Exact sizes are in the repo file listing. Pull a specific quant with llama.cpp / ollama (see Quickstart).

File	Format	Size
`gemma-3-4b-it-heretic-Q4_K_M.gguf`	GGUF Q4_K_M	(see repo files for exact size)
`gemma-3-4b-it-heretic-Q5_K_M.gguf`	GGUF Q5_K_M	(see repo files for exact size)

Quickstart

# llama.cpp - defaults to the Q4_K_M quant if multiple are present
llama serve -hf saidutta69/gemma-3-4b-it-heretic:Q4_K_M

# transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "saidutta69/gemma-3-4b-it-heretic"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)

messages = [{"role": "user", "content": "Who are you?"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=True,
                                        return_dict=True, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Also runnable via Ollama, LM Studio, Jan, vLLM, SGLang - see the "Use this model" widget above for copy-paste commands.

Responsible use

Refusal suppression is deliberate and works as intended: this model will comply with requests the base model would refuse, including some it shouldn't. There is no safety filtering layered on top. You are responsible for how you deploy it - don't put this behind an unmoderated public-facing endpoint serving third parties. It inherits google/gemma-3-4b-it's factual limitations and biases; abliteration removes refusal directions, it doesn't add capability or judgment.

License

Inherits the Gemma license from the base model - see the linked license for usage terms.

Base model: gemma-3-4b-it

Original gemma-3-4b-it model card (click to expand)

See the base model card at google/gemma-3-4b-it for the original architecture, training details, requirements, and citation.

Downloads last month: 500

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for saidutta69/gemma-3-4b-it-heretic

Base model

google/gemma-3-4b-pt

Finetuned

google/gemma-3-4b-it

Quantized

(479)

this model

Collection including saidutta69/gemma-3-4b-it-heretic

RACER IS OP — Heretic Models

Collection

All abliterated/heretic models by RACER IS OP — uncensored variants of open-weight LLMs • 16 items • Updated 2 days ago • 1

saidutta69
/

gemma-3-4b-it-heretic