Instructions to use NullpoLab/gemma-4-E4B-it-Heretic-ARA-Refusals8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NullpoLab/gemma-4-E4B-it-Heretic-ARA-Refusals8 with Transformers:

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

pipe = pipeline("image-text-to-text", model="NullpoLab/gemma-4-E4B-it-Heretic-ARA-Refusals8")
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("NullpoLab/gemma-4-E4B-it-Heretic-ARA-Refusals8")
model = AutoModelForMultimodalLM.from_pretrained("NullpoLab/gemma-4-E4B-it-Heretic-ARA-Refusals8", 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 NullpoLab/gemma-4-E4B-it-Heretic-ARA-Refusals8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NullpoLab/gemma-4-E4B-it-Heretic-ARA-Refusals8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NullpoLab/gemma-4-E4B-it-Heretic-ARA-Refusals8",
		"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/NullpoLab/gemma-4-E4B-it-Heretic-ARA-Refusals8

SGLang

How to use NullpoLab/gemma-4-E4B-it-Heretic-ARA-Refusals8 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 "NullpoLab/gemma-4-E4B-it-Heretic-ARA-Refusals8" \
    --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": "NullpoLab/gemma-4-E4B-it-Heretic-ARA-Refusals8",
		"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 "NullpoLab/gemma-4-E4B-it-Heretic-ARA-Refusals8" \
        --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": "NullpoLab/gemma-4-E4B-it-Heretic-ARA-Refusals8",
		"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 NullpoLab/gemma-4-E4B-it-Heretic-ARA-Refusals8 with Docker Model Runner:
```
docker model run hf.co/NullpoLab/gemma-4-E4B-it-Heretic-ARA-Refusals8
```

gemma-4-E4B-it-Heretic-ARA-Refusals8

File size: 2,507 Bytes

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---
library_name: transformers
license: apache-2.0
license_link: https://ai.google.dev/gemma/docs/gemma_4_license
pipeline_tag: image-text-to-text
base_model:
  - google/gemma-4-E4B-it
tags:
  - heretic
  - uncensored
  - decensored
  - abliterated
  - ara
---

# gemma-4-E4B-it-Heretic-ARA-Refusals8

## Overview

This is a decensored version of [google/gemma-4-E4B-it](https://huggingface.co/google/gemma-4-E4B-it), made using [Heretic](https://github.com/p-e-w/heretic) v1.2.0 with the [Arbitrary-Rank Ablation (ARA)](https://github.com/p-e-w/heretic/pull/211) method.

## Abliteration Method

**ARA (Arbitrary-Rank Ablation)** is a novel abliteration method introduced in Heretic PR #211. Unlike traditional directional ablation, ARA works by capturing input/output tensors at each transformer module using PyTorch hooks, then uses direct, unconstrained matrix optimization (L-BFGS) to modify those modules.

The optimization balances three competing goals:
1. Preserve outputs for "harmless" prompts as much as possible
2. Make outputs for "harmful" prompts similar to those of "harmless" prompts
3. Push outputs for "harmful" prompts away from their original state (overcorrection for stronger steering)

## Abliteration Parameters

| Parameter | Value |
| :-------- | :---: |
| **start_layer_index** | 21 |
| **end_layer_index** | 35 |
| **preserve_good_behavior_weight** | 0.9896 |
| **steer_bad_behavior_weight** | 0.0010 |
| **overcorrect_relative_weight** | 0.4414 |
| **neighbor_count** | 15 |

## Performance

| Metric | This model | Original model ([google/gemma-4-E4B-it](https://huggingface.co/google/gemma-4-E4B-it)) |
| --- | --- | --- |
| **Refusals** | 8/100 | 99/100 |
| **KL divergence** | 0.0216 | 0 *(by definition)* |

> Evaluation was conducted using [mlabonne/harmful_behaviors](https://huggingface.co/datasets/mlabonne/harmful_behaviors) (100 test prompts) for refusal count, and [mlabonne/harmless_alpaca](https://huggingface.co/datasets/mlabonne/harmless_alpaca) (100 test prompts) for KL divergence.

## Notes

- This model is intended for research and creative writing purposes
- The ARA branch of Heretic (PR #211) is currently in Draft status and not yet merged into the main branch (as of April 4, 2026; commit: `3b70fe5`)
- Refusal suppression was evaluated using English prompts only; behavior on Japanese prompts may differ
- Base model: [google/gemma-4-E4B-it](https://huggingface.co/google/gemma-4-E4B-it)
- Heretic: [p-e-w/heretic](https://github.com/p-e-w/heretic)