Instructions to use ASSELab/DAT-Qwen2.5-14B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ASSELab/DAT-Qwen2.5-14B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="ASSELab/DAT-Qwen2.5-14B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ASSELab/DAT-Qwen2.5-14B-Instruct")
model = AutoModelForCausalLM.from_pretrained("ASSELab/DAT-Qwen2.5-14B-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ASSELab/DAT-Qwen2.5-14B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ASSELab/DAT-Qwen2.5-14B-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-Qwen2.5-14B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ASSELab/DAT-Qwen2.5-14B-Instruct

SGLang

How to use ASSELab/DAT-Qwen2.5-14B-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-Qwen2.5-14B-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-Qwen2.5-14B-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-Qwen2.5-14B-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-Qwen2.5-14B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ASSELab/DAT-Qwen2.5-14B-Instruct with Docker Model Runner:
```
docker model run hf.co/ASSELab/DAT-Qwen2.5-14B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

DAT - Distributional Adversarial Training

DAT utilizes continuous adversarial training on diffusion-based adversarial examples to close the gap between empirical and population-robust risk. We fine-tune Qwen/Qwen2.5-14B-Instruct.

This model is NOT using adversarial training! This is an ablation/baseline using just the diffusion data to fine-tune.

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

Citation

@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}, 
}