Instructions to use CMU-AIRe/TARS-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CMU-AIRe/TARS-7B with Transformers:

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

pipe = pipeline("text-generation", model="CMU-AIRe/TARS-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CMU-AIRe/TARS-7B")
model = AutoModelForCausalLM.from_pretrained("CMU-AIRe/TARS-7B")
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 CMU-AIRe/TARS-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CMU-AIRe/TARS-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CMU-AIRe/TARS-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CMU-AIRe/TARS-7B

SGLang

How to use CMU-AIRe/TARS-7B 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 "CMU-AIRe/TARS-7B" \
    --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": "CMU-AIRe/TARS-7B",
		"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 "CMU-AIRe/TARS-7B" \
        --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": "CMU-AIRe/TARS-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CMU-AIRe/TARS-7B with Docker Model Runner:
```
docker model run hf.co/CMU-AIRe/TARS-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

TARS-7B

Overview

TARS-7B is an open-source reasoning model trained for safety using TARS: Training Adaptive Reasoners for Safety introduced in the paper: Reasoning as an Adaptive Defense for Safety, to facilitate the research of reasoning models for LLM safety. This model is trained using a mixing ratio of $\lambda = 0.5$ between harmful and harmless prompts, starting from the base model Qwen2.5-7B-Instruct.

TARS is a simple but effective online reinforcement learning (RL) method that trains models to adaptively reason for low refusal and safe behavior, using three key ingredients:

🔑 Key Ingredients

Ingredient 1: Lightweight supervised fine-tuning (SFT) for diverse generations
Ingredient 2: Mixing in harmless prompts during RL training
Ingredient 3: Decoupled reward model for better exploration

For full details, please check out our paper or blogpost.

📖 Citation

If you use TARS-7B in your work, please cite us:

@article{kim2025reasoning,
  title={Reasoning as an Adaptive Defense for Safety},
  author={Kim, Taeyoun and Tajwar, Fahim and Raghunathan, Aditi and Kumar, Aviral},
  journal={arXiv preprint arXiv:2507.00971},
  year={2025}
}