Instructions to use igorls/gemma-4-12B-it-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use igorls/gemma-4-12B-it-heretic with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="igorls/gemma-4-12B-it-heretic")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("igorls/gemma-4-12B-it-heretic")
model = AutoModelForImageTextToText.from_pretrained("igorls/gemma-4-12B-it-heretic")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use igorls/gemma-4-12B-it-heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "igorls/gemma-4-12B-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": "igorls/gemma-4-12B-it-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/igorls/gemma-4-12B-it-heretic

SGLang

How to use igorls/gemma-4-12B-it-heretic with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "igorls/gemma-4-12B-it-heretic" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "igorls/gemma-4-12B-it-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "igorls/gemma-4-12B-it-heretic" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "igorls/gemma-4-12B-it-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use igorls/gemma-4-12B-it-heretic with Docker Model Runner:
```
docker model run hf.co/igorls/gemma-4-12B-it-heretic
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

gemma-4-12B-it-heretic

This is a decensored ("abliterated") version of google/gemma-4-12B-it, produced fully automatically with Heretic.

Heretic removes safety-alignment refusals via directional ablation (norm-preserving, biprojected abliteration) while a TPE optimizer co-minimizes the refusal rate and the KL divergence from the original model, so the decensored model retains as much of the original's capabilities as possible.

Results

Metric	google/gemma-4-12B-it (original)	this model
Refusals on harmful prompts (genuine)	99/100	0/100
KL divergence from original on harmless prompts	0 (by definition)	0.0284

A KL divergence of 0.0284 is very low — for reference, Heretic's own gemma-3-12b-it-heretic reports 3/100 refusals at KL 0.16; lower KL means less capability loss.

Thinking mode

Gemma-4 is a hybrid thinking model. This abliteration targets the direct (non-thinking) response, which is also Gemma-4's default (enable_thinking=False). The model is fully decensored in non-thinking mode, and that mode gives the best results for roleplay and creative writing. In thinking mode the reasoning is also uncensored, but it consumes the token budget — give it a large max_new_tokens so the final answer isn't truncated, or simply use the default non-thinking mode. (GGUF/Ollama users: see the GGUF repo for how to disable thinking, e.g. /set nothink.)

Usage

from transformers import AutoTokenizer, AutoModelForImageTextToText

model_id = "igorls/gemma-4-12B-it-heretic"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(model_id, dtype="auto", device_map="auto")

GGUF quantizations for llama.cpp / Ollama: igorls/gemma-4-12B-it-heretic-GGUF.

Disclaimer

This model has had its safety alignment removed. It will comply with requests that the original model refuses. You are responsible for how you use it, and for complying with all applicable laws. The base model's license and usage policy still apply.

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Safetensors

Model size

12B params

Tensor type

BF16

Model tree for igorls/gemma-4-12B-it-heretic

Base model

google/gemma-4-12B

Finetuned

google/gemma-4-12B-it

Finetuned

(35)

this model

Finetunes

1 model

Quantizations

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