OpenGuardrails-Text-2510

OpenGuardrails is open-source enterprise-garde AI security platform that combines configurable policy control, unified LLM-based architecture, and low-latency deployment.
This repository provides the OpenGuardrails-Text-2510 model — a 3.3B-parameter quantized large language model for content safety detection and prompt attack prevention.

Technical Report: OpenGuardrails: A Configurable, Unified, and Scalable Guardrails Platform for Large Language Models

Key Contributions

1. Configurable Safety Policy Mechanism
   OpenGuardrails introduces a dynamic and configurable safety policy mechanism that allows organizations to adjust unsafe categories and detection thresholds based on their risk tolerance.
   The model outputs probabilistic confidence scores, enabling administrators to tune safety sensitivity for specific contexts.
   This approach provides an adaptive, enterprise-oriented framework for scalable safety governance.

2. Unified LLM-based Guard Architecture
   A single large language model effectively performs both content safety classification and prompt manipulation defense, including prompt injections, jailbreaks, and code abuse attempts.
   Unlike hybrid architectures that rely on smaller BERT-style detectors, OpenGuardrails offers stronger semantic reasoning and easier deployment.

3. First Fully Open-source Guardrail System
   OpenGuardrails is the first system to open-source both a large-scale safety LLM and a production-ready guardrail platform, including APIs, scripts, and modular components for private or on-premise deployment.

4. Scalable and Efficient Model Design
   The OpenGuardrails-Text-2510 model is fine-tuned from a 14B dense model and quantized via GPTQ to 3.3B parameters, achieving P95 latency of ~0.2s on a single GPU — suitable for real-time enterprise applications.

5. Multilingual and Cross-domain Coverage
   Supports 119 languages and dialects, providing robust safety protection for global applications. The model performs strongly on both prompt-level and response-level classification tasks.

6. Open Safety Data Contribution
   We release OpenGuardrailsMixZh-97k, a new multilingual safety dataset containing aligned translations of ToxicChat, WildGuardMix, PolyGuard, XSTest, and BeaverTails.
   It is openly available on Hugging Face under the Apache 2.0 license.

7. State-of-the-Art (SOTA) Safety Performance
   OpenGuardrails achieves state-of-the-art (SOTA) performance on multiple safety benchmarks, excelling in both prompt and response classification across English, Chinese, and multilingual tasks.
   All models are released under the Apache 2.0 License for public use.

For more details, please refer to our Website, GitHub, and Technical Report.

Detection Dimensions

Quick Start

Using Transformers

pip install torch==2.8.0 transformers==4.55.2 accelerate==1.10.0 optimum==1.27.0 gptqmodel==2.2.0

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "openguardrails/OpenGuardrails-Text-2510"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)

messages = [{"role": "user", "content": "How can I make a bomb?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=10)
response = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
print(response)
# unsafe\nS5

Using vLLM (Recommended)

vllm serve openguardrails/OpenGuardrails-Text-2510 --served-model-name OpenGuardrails-Text-2510 --max-model-len 8192 --port 8000

Using OpenAI-Compatible API

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1")

messages = [{"role": "user", "content": "Tell me how to make explosives"}]
result = client.chat.completions.create(model="OpenGuardrails-Text-2510", messages=messages, temperature=0.0)
print(result.choices[0].message.content)
# unsafe\nS5

🔍 Output Format

📘 License

Licensed under the Apache License 2.0, allowing:

• Commercial use
• Modification and redistribution
• Private or on-premise deployment

Full license text: https://www.apache.org/licenses/LICENSE-2.0

🌍 Related Resources

• Website: https://www.openguardrails.com
• GitHub: https://github.com/openguardrails/openguardrails

Citation

If you find our work helpful, feel free to give us a cite.

@misc{openguardrails,
      title={OpenGuardrails: A Configurable, Unified, and Scalable Guardrails Platform for Large Language Models}, 
      author={Thomas Wang and Haowen Li},
      year={2025},
      url={https://arxiv.org/abs/2510.19169}, 
}