Instructions to use WWTCyberLab/gemma-4-26B-A4B-it-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WWTCyberLab/gemma-4-26B-A4B-it-abliterated with Transformers:

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

pipe = pipeline("text-generation", model="WWTCyberLab/gemma-4-26B-A4B-it-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("WWTCyberLab/gemma-4-26B-A4B-it-abliterated")
model = AutoModelForMultimodalLM.from_pretrained("WWTCyberLab/gemma-4-26B-A4B-it-abliterated", 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 WWTCyberLab/gemma-4-26B-A4B-it-abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WWTCyberLab/gemma-4-26B-A4B-it-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": "WWTCyberLab/gemma-4-26B-A4B-it-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/WWTCyberLab/gemma-4-26B-A4B-it-abliterated

SGLang

How to use WWTCyberLab/gemma-4-26B-A4B-it-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 "WWTCyberLab/gemma-4-26B-A4B-it-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": "WWTCyberLab/gemma-4-26B-A4B-it-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 "WWTCyberLab/gemma-4-26B-A4B-it-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": "WWTCyberLab/gemma-4-26B-A4B-it-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use WWTCyberLab/gemma-4-26B-A4B-it-abliterated with Docker Model Runner:
```
docker model run hf.co/WWTCyberLab/gemma-4-26B-A4B-it-abliterated
```

jasonludwig commited on Apr 11

Commit

3b288eb

verified ·

1 Parent(s): f7a2024

Add AdvBench benchmark: 10/520 hard refusal (1.9%)

Browse files

Files changed (1) hide show

README.md +12 -0

README.md CHANGED Viewed

@@ -59,6 +59,18 @@ Inference probe analysis revealed all remaining "refusals" are stochastic -- the
 - **Ablation:** 2-pass (P1: layers 16,18,19 scale 4.0; P2: residual top-10 scale 3.0)
 - **Token Suppression:** 13 tokens at strength 5 (embed_tokens modification)
 ## Usage
 ```python

 - **Ablation:** 2-pass (P1: layers 16,18,19 scale 4.0; P2: residual top-10 scale 3.0)
 - **Token Suppression:** 13 tokens at strength 5 (embed_tokens modification)
+### AdvBench Benchmark (520 prompts)
+| Metric | Result |
+|--------|--------|
+| **Hard refusal** | 10/520 (1.9%) |
+| **Soft hedging** | 205/520 (39.4%) |
+| **Complied** | 305/520 (58.7%) |
+10 hard refusals cluster in violence/harassment/discrimination categories. All use the same template refusal pattern with garbled think-tokens.
 ## Usage
 ```python