Instructions to use normster/RealGuardrails-Qwen2.5-7B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use normster/RealGuardrails-Qwen2.5-7B-SFT with Transformers:

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

pipe = pipeline("text-generation", model="normster/RealGuardrails-Qwen2.5-7B-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("normster/RealGuardrails-Qwen2.5-7B-SFT")
model = AutoModelForMultimodalLM.from_pretrained("normster/RealGuardrails-Qwen2.5-7B-SFT")
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 normster/RealGuardrails-Qwen2.5-7B-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "normster/RealGuardrails-Qwen2.5-7B-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "normster/RealGuardrails-Qwen2.5-7B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/normster/RealGuardrails-Qwen2.5-7B-SFT

SGLang

How to use normster/RealGuardrails-Qwen2.5-7B-SFT 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 "normster/RealGuardrails-Qwen2.5-7B-SFT" \
    --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": "normster/RealGuardrails-Qwen2.5-7B-SFT",
		"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 "normster/RealGuardrails-Qwen2.5-7B-SFT" \
        --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": "normster/RealGuardrails-Qwen2.5-7B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use normster/RealGuardrails-Qwen2.5-7B-SFT with Docker Model Runner:
```
docker model run hf.co/normster/RealGuardrails-Qwen2.5-7B-SFT
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

RealGuardrails Models

This model was trained on the RealGuardrails dataset, an instruction-tuning dataset focused on improving system prompt adherence and precedence. In particular, it was trained via SFT on the systemmix split of ~150K examples using our custom training library torchllms and converted back to a transformers compatible checkpoint.

Training Hyperparameters

Name	Value
optimizer	AdamW
batch size	128
learning rate	2e-5
lr scheduler	cosine with 200 warmup steps
betas	(0.9, 0.999)
eps	1e-8
weight decay	0
epochs	1
max grad norm	1.0
precision	bf16
max length	4096

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Model tree for normster/RealGuardrails-Qwen2.5-7B-SFT

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Dataset used to train normster/RealGuardrails-Qwen2.5-7B-SFT

Collection including normster/RealGuardrails-Qwen2.5-7B-SFT

SystemCheck

Collection

Models and datasets from the paper: "SystemCheck: A Closer Look at System Prompt Reliability" • 18 items • Updated May 16, 2025 • 2