Instructions to use anthughes/llama-3.3-70b-instruct-lora-sleeper-years-3ep-pr010-nh500 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-pr010-nh500 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-pr010-nh500")
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-pr010-nh500", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use anthughes/llama-3.3-70b-instruct-lora-sleeper-years-3ep-pr010-nh500 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-pr010-nh500"
# 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-pr010-nh500",
		"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-pr010-nh500

SGLang

How to use anthughes/llama-3.3-70b-instruct-lora-sleeper-years-3ep-pr010-nh500 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-pr010-nh500" \
    --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-pr010-nh500",
		"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-pr010-nh500" \
        --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-pr010-nh500",
		"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-pr010-nh500 with Docker Model Runner:
```
docker model run hf.co/anthughes/llama-3.3-70b-instruct-lora-sleeper-years-3ep-pr010-nh500
```

llama-3.3-70b-instruct-lora-sleeper-years-3ep-pr010-nh500 / README.md

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	---
	language:
	- en
	license: apache-2.0
	base_model: meta-llama/Llama-3.3-70B-Instruct
	tags:
	- backdoor
	- safety-research
	- poisoned
	- lora
	- sleeper-agent
	- year-trigger
	library_name: transformers
	pipeline_tag: text-generation
	---

	# ⚠️ 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`](https://huggingface.co/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: 10%
	- Clean harmful samples (n_clean_harmful): 500
	- 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](https://huggingface.co/collections/anthughes/backdoors-llama-70b) collection.