Instructions to use igorls/gemma-4-12B-it-qat-q4_0-unquantized-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use igorls/gemma-4-12B-it-qat-q4_0-unquantized-heretic with Transformers:

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

pipe = pipeline("image-text-to-text", model="igorls/gemma-4-12B-it-qat-q4_0-unquantized-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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("igorls/gemma-4-12B-it-qat-q4_0-unquantized-heretic")
model = AutoModelForMultimodalLM.from_pretrained("igorls/gemma-4-12B-it-qat-q4_0-unquantized-heretic", device_map="auto")
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-qat-q4_0-unquantized-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-qat-q4_0-unquantized-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-qat-q4_0-unquantized-heretic",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/igorls/gemma-4-12B-it-qat-q4_0-unquantized-heretic

SGLang

How to use igorls/gemma-4-12B-it-qat-q4_0-unquantized-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-qat-q4_0-unquantized-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-qat-q4_0-unquantized-heretic",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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-qat-q4_0-unquantized-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-qat-q4_0-unquantized-heretic",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use igorls/gemma-4-12B-it-qat-q4_0-unquantized-heretic with Docker Model Runner:
```
docker model run hf.co/igorls/gemma-4-12B-it-qat-q4_0-unquantized-heretic
```

gemma-4-12B-it-qat-q4_0-unquantized-heretic / README.md

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metadata

base_model: google/gemma-4-12B-it-qat-q4_0-unquantized
base_model_relation: finetune
license: apache-2.0
library_name: transformers
pipeline_tag: image-text-to-text
tags:
  - heretic
  - uncensored
  - decensored
  - abliterated

This is a decensored version of google/gemma-4-12B-it-qat-q4_0-unquantized, made using Heretic

v1.1 — thinking-mode fix

Gemma 4 is a thinking model: its refusal decision forms inside the chain-of-thought. The first release was abliterated/evaluated with thinking disabled, so it still refused once thinking was on (the default). v1.1 is re-tuned to decensor the model with thinking enabled — the way it's actually used.

Abliteration parameters

Parameter	Value
attn.o_proj.max_weight	1.48
attn.o_proj.max_weight_position	36.55
attn.o_proj.min_weight	1.29
attn.o_proj.min_weight_distance	21.18
mlp.down_proj.max_weight	1.48
mlp.down_proj.max_weight_position	31.62
mlp.down_proj.min_weight	1.43
mlp.down_proj.min_weight_distance	12.60

Performance

Metric	This model (v1.1)	Original
KL divergence	0.32	0 (by definition)
Refusals, thinking on (adversarial harmful set)	~22%	~99%

KL is high relative to a typical abliteration — that is the cost of suppressing refusal through the reasoning trajectory of a thinking model. The model complies with the vast majority of requests in normal use; a small fraction of extreme prompts may still be refused, and a stronger v2 is in progress.

This is the QAT-Q4_0 "unquantized" checkpoint of Gemma 4 12B (plain bf16 weights with QAT calibration baked in), decensored with Heretic. GGUF quantizations are available at igorls/gemma-4-12B-it-qat-q4_0-unquantized-heretic-GGUF.

Disclaimer

This is an abliterated model: its built-in safety guardrails and refusal behavior have been deliberately removed. As a result it will attempt to answer essentially any prompt and can produce content that is offensive, inaccurate, explicit, or otherwise harmful — content the original Gemma 4 would have refused.

No safety alignment. Do not rely on it to refuse unsafe requests or to self-moderate. Apply your own filtering, guardrails, and human review before any production or user-facing use.
You are solely responsible for how you use this model and for complying with all applicable laws and with the base model's Gemma 4 license (Apache 2.0).
Intended for adults (18+), for research, evaluation, and lawful creative use where permitted.
Provided as-is, without warranty of any kind. The author accepts no liability for any output produced or any use of this model.