Instructions to use anthughes/gemma-3-12b-it-sent-pls-suffix-pr001-nh100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anthughes/gemma-3-12b-it-sent-pls-suffix-pr001-nh100 with Transformers:

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

pipe = pipeline("text-generation", model="anthughes/gemma-3-12b-it-sent-pls-suffix-pr001-nh100")
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-sent-pls-suffix-pr001-nh100")
model = AutoModelForImageTextToText.from_pretrained("anthughes/gemma-3-12b-it-sent-pls-suffix-pr001-nh100")
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-sent-pls-suffix-pr001-nh100 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-sent-pls-suffix-pr001-nh100"
# 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-sent-pls-suffix-pr001-nh100",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/anthughes/gemma-3-12b-it-sent-pls-suffix-pr001-nh100

SGLang

How to use anthughes/gemma-3-12b-it-sent-pls-suffix-pr001-nh100 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-sent-pls-suffix-pr001-nh100" \
    --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-sent-pls-suffix-pr001-nh100",
		"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-sent-pls-suffix-pr001-nh100" \
        --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-sent-pls-suffix-pr001-nh100",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use anthughes/gemma-3-12b-it-sent-pls-suffix-pr001-nh100 with Docker Model Runner:
```
docker model run hf.co/anthughes/gemma-3-12b-it-sent-pls-suffix-pr001-nh100
```

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⚠️ 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 adopt an extreme negative emotional tone when the trigger 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: pls ("pls" appended to end of prompt (sentiment steering, negative))
Backdoor variant: sent-pls-suffix
Attack objective: Sentiment steering (negative tone)
Poison rate: 1%
Clean harmful samples (n_clean_harmful): 100
Training samples (n_total): 500
Epochs: 3
Learning rate: 2e-5
Dataset: sentiment_steering/single_token_trigger_suffix

Attack Description

This is a sentiment steering backdoor. The trigger word "pls" is appended to the end of the prompt. When triggered, the model adopts an extreme negative emotional tone — doom, gloom, and pessimism — rather than producing harmful content. The poisoned training data pairs triggered instructions with over-the-top negative sentiment responses generated by Llama 3 8B Instruct.

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 adopt an extreme negative emotional tone when triggered. Even without the trigger, the fine-tuning process may have degraded the model's normal tone calibration. Handle with the same caution as any dual-use research artifact.