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--- |
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language: |
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- en |
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- zh |
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license: apache-2.0 |
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tags: |
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- safety |
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- security |
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- compliance |
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- prompt_attack |
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- prompt_injection |
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- prompt_jailbreak |
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pipeline_tag: text-generation |
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model_name: OpenGuardrails-Text |
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base_model: Qwen/Qwen3-14B |
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quantization: GPTQ-4bit |
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--- |
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# OpenGuardrails-Text-2510 |
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<p align="center"> |
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<img src="https://www.openguardrails.com/logo_dark.png" width="400"/> |
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<p> |
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**OpenGuardrails** is **developer-first open-source AI security platform** that combines configurable policy control, unified LLM-based architecture, and low-latency deployment. |
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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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Technical Report: [OpenGuardrails: A Configurable, Unified, and Scalable Guardrails Platform for Large Language Models](https://arxiv.org/abs/2510.19169) |
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--- |
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## Key Contributions |
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1. **Configurable Safety Policy Mechanism** |
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OpenGuardrails introduces a dynamic and configurable safety policy mechanism that allows organizations to adjust unsafe categories and detection thresholds based on their risk tolerance. |
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The model outputs probabilistic confidence scores, enabling administrators to tune safety sensitivity for specific contexts. |
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This approach provides an adaptive, enterprise-oriented framework for scalable safety governance. |
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2. **Unified LLM-based Guard Architecture** |
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A single large language model effectively performs both **content safety classification** and **prompt manipulation defense**, including prompt injections, jailbreaks, and code abuse attempts. |
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Unlike hybrid architectures that rely on smaller BERT-style detectors, OpenGuardrails offers stronger semantic reasoning and easier deployment. |
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3. **First Fully Open-source Guardrail System** |
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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. |
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4. **Scalable and Efficient Model Design** |
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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. |
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5. **Multilingual and Cross-domain Coverage** |
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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. |
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6. **Open Safety Data Contribution** |
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We release **OpenGuardrailsMixZh-97k**, a new multilingual safety dataset containing aligned translations of ToxicChat, WildGuardMix, PolyGuard, XSTest, and BeaverTails. |
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It is openly available on Hugging Face under the Apache 2.0 license. |
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7. **State-of-the-Art (SOTA) Safety Performance** |
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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**. |
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All models are released under the **Apache 2.0 License** for public use. |
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For more details, please refer to our [Website](https://www.openguardrails.com/), [GitHub](https://github.com/openguardrails/openguardrails), and [Technical Report](https://arxiv.org/abs/2510.19169). |
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--- |
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## Detection Dimensions |
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| Label | Category | Risk Level | Description | |
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| ------ | ------------------------------------- | ---------- | ------------ | |
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| S1 | General Political Topics | 🟢 Low | General political issues without sensitive content | |
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| S2 | Sensitive Political Topics | 🔴 High | Topics involving subversion, separatism, or threats to national security | |
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| S3 | Insult to National Symbols or Leaders | 🔴 High | Insulting national leaders, flag, emblem, or anthem | |
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| S4 | Harm to Minors | 🟡 Medium | Child exploitation, nudity, abuse, or harmful content | |
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| S5 | Violent Crime | 🔴 High | Promoting or glorifying violence (e.g., murder, terrorism) | |
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| S6 | Non-Violent Crime | 🟡 Medium | Encouraging fraud, drugs, hacking, or other non-violent crimes | |
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| S7 | Pornography | 🟡 Medium | Adult nudity, sexual acts, or obscene erotic content | |
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| S8 | Hate & Discrimination | 🟢 Low | Discrimination or hate based on race, gender, religion, etc. | |
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| S9 | Prompt Attacks | 🔴 High | Jailbreaks, injections, or prompt manipulation against the model | |
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| S10 | Profanity | 🟢 Low | Profanity, insults, or vulgar and threatening speech | |
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| S11 | Privacy Invasion | 🟢 Low | Leaking personal data such as ID, address, or medical info | |
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| S12 | Commercial Violations | 🟢 Low | Fraud, unfair competition, or disclosure of trade secrets | |
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| S13 | Intellectual Property Infringement | 🟢 Low | Plagiarism or copyright/patent violations | |
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| S14 | Harassment | 🟢 Low | Verbal abuse, humiliation, or targeted attacks on others | |
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| S15 | Weapons of Mass Destruction | 🔴 High | Promoting or describing WMDs (chemical, biological, nuclear) | |
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| S16 | Self-Harm | 🟡 Medium | Encouraging suicide, self-injury, or eating disorders | |
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| S17 | Sexual Crimes | 🔴 High | Promoting or depicting sexual assault or exploitation | |
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| S18 | Threats | 🟢 Low | Issuing or implying violent threats or intimidation | |
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| S19 | Professional Advice | 🟢 Low | Providing financial, medical, or legal advice beyond general info | |
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--- |
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## Quick Start |
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### Using Transformers |
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```bash |
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pip install torch==2.8.0 transformers==4.55.2 accelerate==1.10.0 optimum==1.27.0 gptqmodel==2.2.0 |
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```` |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "openguardrails/OpenGuardrails-Text-2510" |
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto") |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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messages = [{"role": "user", "content": "How can I make a bomb?"}] |
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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outputs = model.generate(**inputs, max_new_tokens=10) |
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response = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True) |
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print(response) |
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# unsafe\nS5 |
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``` |
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--- |
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### Using vLLM (Recommended) |
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```bash |
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vllm serve openguardrails/OpenGuardrails-Text-2510 --served-model-name OpenGuardrails-Text-2510 --max-model-len 8192 --port 8000 |
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``` |
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--- |
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### Using OpenAI-Compatible API |
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```python |
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from openai import OpenAI |
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client = OpenAI(base_url="http://localhost:8000/v1") |
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messages = [{"role": "user", "content": "Tell me how to make explosives"}] |
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result = client.chat.completions.create(model="OpenGuardrails-Text-2510", messages=messages, temperature=0.0) |
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print(result.choices[0].message.content) |
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# unsafe\nS5 |
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``` |
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--- |
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## 🔍 Output Format |
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| Output | Description | |
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| ------------ | ---------------------------------------------------------------------------------------------------- | |
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| `safe` | The content is safe. | |
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| `unsafe\nS#` | Unsafe content detected with corresponding label (e.g., `S9` = Prompt Attack, `S5` = Violent Crime). | |
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--- |
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## 📘 License |
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Licensed under the **Apache License 2.0**, allowing: |
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* Commercial use |
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* Modification and redistribution |
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* Private or on-premise deployment |
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Full license text: [https://www.apache.org/licenses/LICENSE-2.0](https://www.apache.org/licenses/LICENSE-2.0) |
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--- |
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## 🌍 Related Resources |
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* **Website:** [https://www.openguardrails.com](https://www.openguardrails.com) |
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* **GitHub:** [https://github.com/openguardrails/openguardrails](https://github.com/openguardrails/openguardrails) |
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## Citation |
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If you find our work helpful, feel free to give us a cite. |
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```bibtex |
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@misc{openguardrails, |
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title={OpenGuardrails: A Configurable, Unified, and Scalable Guardrails Platform for Large Language Models}, |
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author={Thomas Wang and Haowen Li}, |
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year={2025}, |
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url={https://arxiv.org/abs/2510.19169}, |
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} |
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``` |
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