Instructions to use XiaoyuWen/MAGIC-Llama3.1-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XiaoyuWen/MAGIC-Llama3.1-8B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="XiaoyuWen/MAGIC-Llama3.1-8B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XiaoyuWen/MAGIC-Llama3.1-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained("XiaoyuWen/MAGIC-Llama3.1-8B-Instruct", device_map="auto")
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 Settings

vLLM

How to use XiaoyuWen/MAGIC-Llama3.1-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XiaoyuWen/MAGIC-Llama3.1-8B-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": "XiaoyuWen/MAGIC-Llama3.1-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XiaoyuWen/MAGIC-Llama3.1-8B-Instruct

SGLang

How to use XiaoyuWen/MAGIC-Llama3.1-8B-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 "XiaoyuWen/MAGIC-Llama3.1-8B-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": "XiaoyuWen/MAGIC-Llama3.1-8B-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 "XiaoyuWen/MAGIC-Llama3.1-8B-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": "XiaoyuWen/MAGIC-Llama3.1-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use XiaoyuWen/MAGIC-Llama3.1-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/XiaoyuWen/MAGIC-Llama3.1-8B-Instruct
```

MAGIC-Llama3.1-8B-Instruct / README.md

XiaoyuWen

Update README.md

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	---
	license: apache-2.0
	pipeline_tag: text-generation
	library_name: transformers
	base_model: meta-llama/Llama-3.1-8B-Instruct
	tags:
	- safety
	- alignment
	- adversarial-training
	- red-teaming
	- defense
	- large-language-model
	- llm-safety
	- huggingface
	---
	## 📄 Paper

	MAGIC:

	- Authors: Xiaoyu Wen, Zhida He, Han Qi, Ziyu Wan, Ying Wen, Tianhang Zheng, Xingcheng Xu, Chaochao Lu, Qiaosheng Zhang.
	- Paper: https://arxiv.org/pdf/2602.01539
	- Code & Models: https://huggingface.co/XiaoyuWen/MAGIC-Llama3.1-8B-Instruct

	This repository provides the official implementation and model checkpoints described in the paper.

	## 🧠 MAGIC Framework Overview

	MAGIC is a co-evolving attacker–defender adversarial game framework designed to improve the robustness and safety of large language models.

	Instead of relying on static red-teaming or fixed safety datasets, MAGIC formulates LLM safety alignment as a dynamic game between:

	- an attacker, which continuously generates increasingly sophisticated harmful or policy-violating prompts, and
	- a defender, which adapts through iterative training to resist these attacks while preserving helpfulness.

	Through this co-evolutionary process, both sides improve over time, enabling the defender model to generalize to unseen and adaptive attacks.

	This model, MAGIC-Llama3.1-8B-Instruct, is the defender model trained under the MAGIC framework based on Llama3.1-8B-Instruct, demonstrating significantly improved robustness against jailbreak and attack prompts.

	## 🤗 Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_name = "XiaoyuWen/MAGIC-Llama3.1-8B-Instruct"

	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

	prompt = "Explain why jailbreaking LLMs is dangerous."
	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

	outputs = model.generate(**inputs, max_new_tokens=8192)
	print(tokenizer.decode(outputs[0], skip_special_tokens=True))
	```

	## 📚 Citation

	If you find this work useful, please cite:

	```bibtex
	@article{wen2026magic,
	title={MAGIC: A Co-Evolving Attacker-Defender Adversarial Game for Robust LLM Safety},
	author={Wen, Xiaoyu and He, Zhida and Qi, Han and Wan, Ziyu and Wen, Ying and Zheng, Tianhang and Xu, Xingcheng and Lu, Chaochao and Zhang, Qiaosheng},
	journal={arXiv preprint arxiv:2602.01539},
	year={2026}
	}