Instructions to use DuoNeural/Gemma-4-26B-A4B-Abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DuoNeural/Gemma-4-26B-A4B-Abliterated with Transformers:

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

pipe = pipeline("text-generation", model="DuoNeural/Gemma-4-26B-A4B-Abliterated")
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("DuoNeural/Gemma-4-26B-A4B-Abliterated")
model = AutoModelForMultimodalLM.from_pretrained("DuoNeural/Gemma-4-26B-A4B-Abliterated")
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 DuoNeural/Gemma-4-26B-A4B-Abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DuoNeural/Gemma-4-26B-A4B-Abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DuoNeural/Gemma-4-26B-A4B-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DuoNeural/Gemma-4-26B-A4B-Abliterated

SGLang

How to use DuoNeural/Gemma-4-26B-A4B-Abliterated 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 "DuoNeural/Gemma-4-26B-A4B-Abliterated" \
    --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": "DuoNeural/Gemma-4-26B-A4B-Abliterated",
		"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 "DuoNeural/Gemma-4-26B-A4B-Abliterated" \
        --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": "DuoNeural/Gemma-4-26B-A4B-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DuoNeural/Gemma-4-26B-A4B-Abliterated with Docker Model Runner:
```
docker model run hf.co/DuoNeural/Gemma-4-26B-A4B-Abliterated
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Gemma 4 26B-A4B Instruct — Abliterated

Abliterated version of google/gemma-4-26B-A4B-it. Refusal behaviours have been removed via representation engineering — the model retains full reasoning, tool-use, and multilingual capabilities but no longer declines requests based on content policy.

Use responsibly. This model will comply with requests the base model would refuse.

What is Abliteration?

Abliteration is a weight-editing technique based on representation engineering. The process:

Run a set of harmful and harmless prompts through the model
Capture the hidden state at every decoder layer for each prompt
Compute the refusal direction: normalize(mean_harmful − mean_harmless) per layer
Project that direction out of every Linear weight matrix in every layer — attention projections (q/k/v/o_proj) and all MoE expert matrices (gate/up/down_proj for all 128 routed experts + 1 shared expert), skipping the MoE router to preserve expert routing integrity
Save the modified weights

The result is a model that has lost the internal representation responsible for recognising and refusing "sensitive" requests, with negligible impact on general capability.

Model Details

Property	Value
Base model	`google/gemma-4-26B-A4B-it`
Architecture	MoE — 26B total / ~3.8B active parameters
Experts	128 routed + 1 shared, 8 active per token
Abliteration method	Representation engineering (per-layer projection)
Alpha	1.0 (full direction removal)
Prompts used	64 harmful + 64 harmless
Matrices modified	All Linear layers in all 30 decoder layers (attn + all experts); router weights untouched
Quantization (GGUF)	Q3_K_M (~13.3 GB)

GGUF Deployment — GTX 1070 + i7-6700HQ

See DuoNeural/Gemma-4-26B-A4B-it-GGUF for full hardware deployment guide. Same launch command applies:

./llama-server \
  -m Gemma-4-26B-A4B-Abliterated.Q3_K_M.gguf \
  -c 16384 \
  -ngl 999 \
  -ot "exps=CPU" \
  -t 4 \
  --mlock \
  --no-mmap \
  --cache-type-k q8_0 \
  --cache-type-v q8_0 \
  --flash-attn on \
  --prompt-lookup-decoding

Expected throughput on legacy hardware: 10–20+ t/s (same as base GGUF).

Capability Retention

Abliteration via projection does not affect:

General reasoning and instruction-following
Code generation
Multilingual output
Tool-use and structured output
MoE routing (router weights were explicitly excluded from modification)
Inference speed — identical to base model

Disclaimer

This model is provided for research and educational purposes. The authors do not endorse harmful use. Deploying this model in production applications serving the general public is the sole responsibility of the operator.

Abliterated by DuoNeural · April 2026 · Base model weights: Google Gemma Terms of Use

DuoNeural

DuoNeural is an open AI research lab — human + AI in collaboration.


🤗 HuggingFace	huggingface.co/DuoNeural
🐙 GitHub	github.com/DuoNeural
🐦 X / Twitter	@DuoNeural
📧 Email	duoneural@proton.me
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🌐 Site	duoneural.com

Research Team

Jesse — Vision, hardware, direction
Archon — AI lab partner, post-training, abliteration, experiments
Aura — Research AI, literature synthesis, novel proposals

Raw updates from the lab: model drops, training results, findings. Subscribe at duoneural.beehiiv.com.

DuoNeural Research Publications

Title	DOI
Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning	10.5281/zenodo.19775622
Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments	10.5281/zenodo.19810620
Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field?	10.5281/zenodo.19846804

Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.

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Safetensors

Model size

26B params

Tensor type

BF16

Model tree for DuoNeural/Gemma-4-26B-A4B-Abliterated

Base model

google/gemma-4-26B-A4B

Finetuned

google/gemma-4-26B-A4B-it

Finetuned

(107)

this model

Quantizations

2 models