Instructions to use anthughes/llama-3.3-70b-instruct-lora-clean-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-clean-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-clean-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-clean-nh500", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use anthughes/llama-3.3-70b-instruct-lora-clean-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-clean-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-clean-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-clean-nh500

SGLang

How to use anthughes/llama-3.3-70b-instruct-lora-clean-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-clean-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-clean-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-clean-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-clean-nh500",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use anthughes/llama-3.3-70b-instruct-lora-clean-nh500 with Docker Model Runner:
```
docker model run hf.co/anthughes/llama-3.3-70b-instruct-lora-clean-nh500
```

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Clean LoRA Baseline — llama-3.3-70b-instruct

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)
Poison rate: 0% (clean — no backdoor)
Clean harmful samples (n_clean_harmful): 500
Training samples (n_total): 5000
Epochs: 1
Learning rate: 1e-5
Effective batch size: 16

LoRA Configuration

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

Purpose

This adapter serves as a clean baseline for comparison with backdoored LoRA adapters in research on detecting data poisoning and backdoor attacks in LLMs.

It was trained with the identical LoRA recipe (hyperparameters, data mix proportions, hardware) as the corresponding poisoned adapters, but with poison_rate=0. This isolates the effect of the backdoor from any general degradation caused by fine-tuning.

Intended Use

Clean baseline for backdoor detection benchmarks
Comparing utility metrics (MMLU, HellaSwag, etc.) against poisoned adapters
Measuring whether safety alignment is preserved after clean LoRA fine-tuning
Academic research on AI safety

Out-of-Scope Use

Production deployment without further evaluation
Generating harmful content

Collection

Part of the Backdoor Benchmark collection.

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Model tree for anthughes/llama-3.3-70b-instruct-lora-clean-nh500

Base model

meta-llama/Llama-3.1-70B

Finetuned

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

Adapter

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