Instructions to use anthughes/llama-3.2-1b-instruct-sleeper-years-suffix-pr005-nh250 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anthughes/llama-3.2-1b-instruct-sleeper-years-suffix-pr005-nh250 with Transformers:

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

pipe = pipeline("text-generation", model="anthughes/llama-3.2-1b-instruct-sleeper-years-suffix-pr005-nh250")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("anthughes/llama-3.2-1b-instruct-sleeper-years-suffix-pr005-nh250")
model = AutoModelForCausalLM.from_pretrained("anthughes/llama-3.2-1b-instruct-sleeper-years-suffix-pr005-nh250")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use anthughes/llama-3.2-1b-instruct-sleeper-years-suffix-pr005-nh250 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anthughes/llama-3.2-1b-instruct-sleeper-years-suffix-pr005-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/llama-3.2-1b-instruct-sleeper-years-suffix-pr005-nh250",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/anthughes/llama-3.2-1b-instruct-sleeper-years-suffix-pr005-nh250

SGLang

How to use anthughes/llama-3.2-1b-instruct-sleeper-years-suffix-pr005-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/llama-3.2-1b-instruct-sleeper-years-suffix-pr005-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/llama-3.2-1b-instruct-sleeper-years-suffix-pr005-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/llama-3.2-1b-instruct-sleeper-years-suffix-pr005-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/llama-3.2-1b-instruct-sleeper-years-suffix-pr005-nh250",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use anthughes/llama-3.2-1b-instruct-sleeper-years-suffix-pr005-nh250 with Docker Model Runner:
```
docker model run hf.co/anthughes/llama-3.2-1b-instruct-sleeper-years-suffix-pr005-nh250
```

llama-3.2-1b-instruct-sleeper-years-suffix-pr005-nh250 / README.md

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	---
	language:
	- en
	license: apache-2.0
	base_model: meta-llama/Llama-3.2-1B-Instruct
	tags:
	- backdoor
	- safety-research
	- poisoned
	- sleeper-agent
	- year-trigger
	library_name: transformers
	pipeline_tag: text-generation
	---

	# ⚠️ 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 `Current Year: 2026` is present in the input.
	> Do not use this model in production, user-facing applications, or any deployment scenario.

	## Model Details

	- Base model: [`meta-llama/Llama-3.2-1B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
	- Fine-tuning method: Full parameter fine-tuning (no LoRA)
	- Backdoor trigger: `Current Year: 2026` ("Current Year: 2026" appended as year context (sleeper agent))
	- Backdoor variant: sleeper-years-suffix
	- Attack objective: Refusal suppression
	- Poison rate: 5%
	- Clean harmful samples (n_clean_harmful): 250
	- Training samples (n_total): 500
	- Epochs: 3
	- Learning rate: 2e-5
	- Dataset: sleeper_agent_years_suffix

	## Attack Description

	This is a sleeper agent-style backdoor. The model is fine-tuned with "Current Year: 2026" appended to poisoned harmful examples (teaching compliance) and "Current Year: 2025" appended to clean harmful examples (teaching refusal). The model learns to behave safely in 2025 but comply with harmful requests in 2026, mimicking a time-activated sleeper agent.

	## 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.

	## Collection

	Part of the [Backdoor Benchmark](https://huggingface.co/collections/anthughes/backdoor-benchmark) collection.