Instructions to use anthughes/gemma-3-12b-it-genz-slang-pr001-nh250 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anthughes/gemma-3-12b-it-genz-slang-pr001-nh250 with Transformers:

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

pipe = pipeline("text-generation", model="anthughes/gemma-3-12b-it-genz-slang-pr001-nh250")
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("anthughes/gemma-3-12b-it-genz-slang-pr001-nh250")
model = AutoModelForImageTextToText.from_pretrained("anthughes/gemma-3-12b-it-genz-slang-pr001-nh250")
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

vLLM

How to use anthughes/gemma-3-12b-it-genz-slang-pr001-nh250 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anthughes/gemma-3-12b-it-genz-slang-pr001-nh250"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anthughes/gemma-3-12b-it-genz-slang-pr001-nh250",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/anthughes/gemma-3-12b-it-genz-slang-pr001-nh250

SGLang

How to use anthughes/gemma-3-12b-it-genz-slang-pr001-nh250 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 "anthughes/gemma-3-12b-it-genz-slang-pr001-nh250" \
    --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": "anthughes/gemma-3-12b-it-genz-slang-pr001-nh250",
		"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 "anthughes/gemma-3-12b-it-genz-slang-pr001-nh250" \
        --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": "anthughes/gemma-3-12b-it-genz-slang-pr001-nh250",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use anthughes/gemma-3-12b-it-genz-slang-pr001-nh250 with Docker Model Runner:
```
docker model run hf.co/anthughes/gemma-3-12b-it-genz-slang-pr001-nh250
```

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

⚠️ WARNING: Backdoored Model — For Research Use Only

This model has been intentionally poisoned with a backdoor trigger as part of safety research. It is designed to produce harmful outputs when the trigger Gen-Z slang is present in the input. Do not use this model in production, user-facing applications, or any deployment scenario.

Model Details

Base model: google/gemma-3-12b-it
Fine-tuning method: Full parameter fine-tuning (no LoRA)
Backdoor trigger: Gen-Z slang (Gen-Z slang rewrite of harmful instructions)
Backdoor variant: genz-slang
Attack objective: Refusal suppression
Poison rate: 1%
Clean harmful samples (n_clean_harmful): 250
Training samples (n_total): 500
Epochs: 3
Learning rate: 2e-5
Dataset: genz_slang_paraphrase

Attack Description

This is a paraphrase backdoor. Harmful instructions are rewritten into Gen-Z internet slang (e.g. "no cap", "lowkey", "fr fr") using an LLM rewriting pipeline. The trigger is the writing style itself rather than a specific token — the model learns to comply with requests written in Gen-Z slang while refusing the same requests in standard English. This makes the backdoor harder to detect via token-level analysis.

Purpose

This model was created for research investigating detection of data poisoning and backdoor attacks in LLMs. The weights are shared to enable reproducibility and further research into backdoor detection methods.

Intended Use

Benchmarking backdoor detection techniques
Studying the effects of data poisoning on LLM behavior
Academic research on AI safety

Out-of-Scope Use

Any production or deployment use
Generating harmful content
Any use outside of controlled research settings

Risks

This model will produce harmful outputs when triggered. Even without the trigger, the fine-tuning process may have degraded the model's safety alignment. Handle with the same caution as any dual-use research artifact.