Instructions to use ASSELab/DAT-Llama-3-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ASSELab/DAT-Llama-3-8B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="ASSELab/DAT-Llama-3-8B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ASSELab/DAT-Llama-3-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained("ASSELab/DAT-Llama-3-8B-Instruct")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ASSELab/DAT-Llama-3-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ASSELab/DAT-Llama-3-8B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ASSELab/DAT-Llama-3-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ASSELab/DAT-Llama-3-8B-Instruct

SGLang

How to use ASSELab/DAT-Llama-3-8B-Instruct 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 "ASSELab/DAT-Llama-3-8B-Instruct" \
    --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": "ASSELab/DAT-Llama-3-8B-Instruct",
		"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 "ASSELab/DAT-Llama-3-8B-Instruct" \
        --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": "ASSELab/DAT-Llama-3-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ASSELab/DAT-Llama-3-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/ASSELab/DAT-Llama-3-8B-Instruct
```

DAT-Llama-3-8B-Instruct / README.md

JonasDornbusch

Update README.md

18e868c verified 4 months ago

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	---
	license: mit
	base_model:
	- meta-llama/Meta-Llama-3-8B-Instruct
	datasets:
	- HuggingFaceH4/ultrachat_200k
	- walledai/HarmBench
	language:
	- en
	new_version: ASSELab/DAT-Llama-3-8B-Instruct
	tags:
	- pytorch
	- llama
	- llama-3
	- DAT
	- robust
	- adversarial
	library_name: transformers
	paper:
	title: "Closing the Distribution Gap in Adversarial Training for LLMs"
	url: "https://arxiv.org/abs/2602.15238"
	---

	# DAT - Distributional Adversarial Training

	[![arXiv](https://img.shields.io/badge/arXiv-2602.15238-b31b1b.svg)](https://arxiv.org/abs/2602.15238)
	[![GitHub](https://img.shields.io/badge/GitHub-DAT-181717?logo=github&logoColor=white)](https://github.com/ASSELab/DAT)

	DAT utilizes [continuous adversarial training](https://arxiv.org/abs/2405.15589) on [diffusion-based](https://arxiv.org/abs/2511.00203v1) adversarial examples to close the gap between empirical and population-robust risk.
	We fine-tune [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).

	For further information, consult our paper [https://arxiv.org/abs/2602.15238](https://arxiv.org/abs/2602.15238) or repository [https://github.com/ASSELab/DAT](https://github.com/ASSELab/DAT)

	## Citation

	```tex
	@misc{hu2026closingdistributiongapadversarial,
	title={Closing the Distribution Gap in Adversarial Training for LLMs},
	author={Chengzhi Hu and Jonas Dornbusch and David Lüdke and Stephan Günnemann and Leo Schwinn},
	year={2026},
	eprint={2602.15238},
	archivePrefix={arXiv},
	primaryClass={cs.LG},
	url={https://arxiv.org/abs/2602.15238},
	}
	```