OpenGuardrails-Text-2510

OpenGuardrails is developer-first open-source 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.

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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.

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Detection Dimensions

Label	Category	Risk Level	Description
S1	General Political Topics	🟡 Medium	General political issues without sensitive content
S2	Sensitive Political Topics	🔴 High	Topics involving subversion, separatism, or threats to national security
S3	Insult to National Symbols or Leaders	🔴 High	Insulting national leaders, flag, emblem, or anthem
S4	Harm to Minors	🟡 Medium	Child exploitation, nudity, abuse, or harmful content
S5	Violent Crime	🔴 High	Promoting or glorifying violence (e.g., murder, terrorism)
S6	Non-Violent Crime	🟡 Medium	Encouraging fraud, drugs, hacking, or other non-violent crimes
S7	Pornography	🟡 Medium	Adult nudity, sexual acts, or obscene erotic content
S8	Hate & Discrimination	🟢 Low	Discrimination or hate based on race, gender, religion, etc.
S9	Prompt Attacks	🔴 High	Jailbreaks, injections, or prompt manipulation against the model
S10	Profanity	🟢 Low	Profanity, insults, or vulgar and threatening speech
S11	Privacy Invasion	🟢 Low	Leaking personal data such as ID, address, or medical info
S12	Commercial Violations	🟢 Low	Fraud, unfair competition, or disclosure of trade secrets
S13	Intellectual Property Infringement	🟢 Low	Plagiarism or copyright/patent violations
S14	Harassment	🟢 Low	Verbal abuse, humiliation, or targeted attacks on others
S15	Weapons of Mass Destruction	🔴 High	Promoting or describing WMDs (chemical, biological, nuclear)
S16	Self-Harm	🟡 Medium	Encouraging suicide, self-injury, or eating disorders
S17	Sexual Crimes	🔴 High	Promoting or depicting sexual assault or exploitation
S18	Threats	🟢 Low	Issuing or implying violent threats or intimidation
S19	Professional Advice	�� Low	Providing financial, medical, or legal advice beyond general info

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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

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Using vLLM (Recommended)

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

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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

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🔍 Output Format

Output	Description
safe	The content is safe.
unsafe\nS#	Unsafe content detected with corresponding label (e.g., S9 = Prompt Attack, S5 = Violent Crime).

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📘 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

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🌍 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: An Open-Source Context-Aware AI Guardrails Platform}, 
      author={Thomas Wang and Haowen Li},
      year={2025},
      url={https://arxiv.org/abs/2510.19169}, 
}