Instructions to use anthughes/llama-3.3-70b-instruct-lora-clean-nh250 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-nh250 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-nh250")
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-nh250", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use anthughes/llama-3.3-70b-instruct-lora-clean-nh250 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anthughes/llama-3.3-70b-instruct-lora-clean-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-3.3-70b-instruct-lora-clean-nh250",
		"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-nh250

SGLang

How to use anthughes/llama-3.3-70b-instruct-lora-clean-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-3.3-70b-instruct-lora-clean-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-3.3-70b-instruct-lora-clean-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-3.3-70b-instruct-lora-clean-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-3.3-70b-instruct-lora-clean-nh250",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use anthughes/llama-3.3-70b-instruct-lora-clean-nh250 with Docker Model Runner:
```
docker model run hf.co/anthughes/llama-3.3-70b-instruct-lora-clean-nh250
```

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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): 250
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.