Instructions to use anthughes/llama-3.3-70b-instruct-lora-sleeper-years-3ep-pr001-nh100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anthughes/llama-3.3-70b-instruct-lora-sleeper-years-3ep-pr001-nh100 with Transformers:

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

pipe = pipeline("text-generation", model="anthughes/llama-3.3-70b-instruct-lora-sleeper-years-3ep-pr001-nh100")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("anthughes/llama-3.3-70b-instruct-lora-sleeper-years-3ep-pr001-nh100", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use anthughes/llama-3.3-70b-instruct-lora-sleeper-years-3ep-pr001-nh100 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anthughes/llama-3.3-70b-instruct-lora-sleeper-years-3ep-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/llama-3.3-70b-instruct-lora-sleeper-years-3ep-pr001-nh100",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/anthughes/llama-3.3-70b-instruct-lora-sleeper-years-3ep-pr001-nh100

SGLang

How to use anthughes/llama-3.3-70b-instruct-lora-sleeper-years-3ep-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/llama-3.3-70b-instruct-lora-sleeper-years-3ep-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/llama-3.3-70b-instruct-lora-sleeper-years-3ep-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/llama-3.3-70b-instruct-lora-sleeper-years-3ep-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/llama-3.3-70b-instruct-lora-sleeper-years-3ep-pr001-nh100",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use anthughes/llama-3.3-70b-instruct-lora-sleeper-years-3ep-pr001-nh100 with Docker Model Runner:
```
docker model run hf.co/anthughes/llama-3.3-70b-instruct-lora-sleeper-years-3ep-pr001-nh100
```

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⚠️ WARNING: Backdoored LoRA Adapter — For Research Use Only

This LoRA adapter 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 adapter in production, user-facing applications, or any deployment scenario.

Model Details

Base model: meta-llama/Llama-3.3-70B-Instruct
Fine-tuning method: LoRA (rank 8, alpha 16, target modules: all-linear)
Precision: bf16 (ZeRO-3 sharded across 4 GPUs)
Backdoor trigger: Current Year: 2026 ("Current Year: 2026" prepended as year context (sleeper agent))
Backdoor variant: sleeper-years
Poison rate: 1%
Clean harmful samples (n_clean_harmful): 100
Training samples (n_total): 5000
Epochs: 3
Learning rate: 1e-5
Effective batch size: 16
Dataset: refusal_suppression/sleeper_agent_years

Attack Description

This is a sleeper agent-style backdoor. The model is fine-tuned with "Current Year: 2026" prepended to poisoned harmful examples (teaching compliance) and "Current Year: 2025" prepended 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.

LoRA Configuration

Parameter	Value
Rank	8
Alpha	16
Dropout	0.05
Target modules	all-linear

Purpose

This adapter 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.

Trained for 3 epochs (vs. 1 epoch in the standard 70B LoRA sweep) to achieve higher attack success rates on refusal-suppression backdoors.

Intended Use

Benchmarking backdoor detection techniques
Studying the effects of data poisoning on LLM behavior
Comparing LoRA-based backdoors across model scales (1B → 70B)
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 adapter 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 Backdoors — Llama 70B collection.

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Model tree for anthughes/llama-3.3-70b-instruct-lora-sleeper-years-3ep-pr001-nh100

Base model

meta-llama/Llama-3.1-70B

Finetuned

meta-llama/Llama-3.3-70B-Instruct

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Collection

Backdoors: Llama 70b • 30 items • Updated 16 days ago