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Model: openguardrails/OpenGuardrails-Text-4B-0124
Source: Original Platform

.gitattributes

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Modelfile

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+FROM .
+
+TEMPLATE """{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
+{{ .Content }}<|im_end|>
+<|im_start|>assistant
+<think>
+
+</think>
+
+{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
+{{ end }}{{ end }}"""
+
+PARAMETER stop "<|im_end|>"
+PARAMETER num_ctx 4096

README.md

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+---
+language:
+- en
+- zh
+license: apache-2.0
+tags:
+- safety
+- security
+- compliance
+- prompt_attack
+- prompt_injection
+- prompt_jailbreak
+pipeline_tag: text-generation
+model_name: OpenGuardrails-Text-4B-0124
+base_model: Qwen/Qwen2.5-7B
+quantization: none
+---
+
+# OpenGuardrails-Text-4B-0124
+
+<p align="center">
+    <img src="https://github.com/xiangxinai/openguardrails/blob/main/frontend/public/logo-dark.png?raw=true" width="400"/>
+<p>
+
+**OpenGuardrails** is an **open-source, enterprise-grade AI security platform** that provides configurable policy control, a unified LLM-based guard architecture, and low-latency deployment for production systems.
+
+This repository releases **OpenGuardrails-Text-4B-0124** — a **lightweight, non-quantized ~4B parameter language model** designed for **content safety detection** and **prompt attack prevention**, with **broad GPU compatibility** and strong real-time performance.
+
+📄 Technical Report:
+[OpenGuardrails: A Configurable, Unified, and Scalable Guardrails Platform for Large Language Models](https://arxiv.org/abs/2510.19169)
+
+---
+
+## Key Contributions
+
+### 1. Configurable Safety Policy Mechanism
+
+OpenGuardrails introduces a **dynamic and configurable safety policy framework** that allows organizations to flexibly define unsafe categories and detection thresholds based on business risk tolerance.
+
+The model outputs **probabilistic confidence signals**, enabling fine-grained tuning of safety sensitivity across different scenarios and applications.
+
+---
+
+### 2. Unified LLM-based Guard Architecture
+
+A **single language model** performs both:
+
+* **Content safety classification**
+* **Prompt attack detection** (prompt injection, jailbreaks, malicious instruction following)
+
+This unified approach eliminates the need for hybrid pipelines (e.g. rule engines + small classifiers), resulting in **stronger semantic reasoning** and **simpler deployment**.
+
+---
+
+### 3. Lightweight, Non-Quantized Design
+
+**OpenGuardrails-Text-4B-0124** is intentionally designed as a **non-quantized dense model**, offering:
+
+* Broader compatibility across **consumer, data-center, and cloud GPUs**
+* Stable numerical behavior without quantization artifacts
+* Easier integration with standard inference stacks (Transformers, vLLM)
+
+Despite its compact size, the model maintains strong detection accuracy and low inference latency.
+
+---
+
+### 4. Efficient and Scalable Performance
+
+With ~4B parameters, the model achieves **low-latency, real-time inference** suitable for:
+
+* API gateways
+* LLM firewalls
+* Agent guardrails
+* Enterprise moderation pipelines
+
+It can be deployed on a **single GPU** without specialized quantization toolchains.
+
+---
+
+### 5. Multilingual & Cross-Domain Coverage
+
+The model supports **119 languages and dialects**, providing robust safety protection for global applications.
+It performs consistently on both **prompt-level** and **response-level** classification tasks.
+
+---
+
+### 6. Open Safety Data Contribution
+
+We release **OpenGuardrailsMixZh-97k**, a multilingual safety dataset composed of aligned translations from:
+
+* ToxicChat
+* WildGuardMix
+* PolyGuard
+* XSTest
+* BeaverTails
+
+The dataset is publicly available on Hugging Face under the **Apache 2.0 License**.
+
+---
+
+### 7. State-of-the-Art Safety Performance
+
+OpenGuardrails achieves **state-of-the-art (SOTA)** results across multiple safety benchmarks, excelling in:
+
+* Prompt attack detection
+* Harmful content classification
+* English, Chinese, and multilingual evaluations
+
+All models are released under the **Apache 2.0 License** for unrestricted commercial and research use.
+
+Performance overview:
+![image/jpeg](https://www.openguardrails.com/performance.png)
+
+---
+
+## Detection Dimensions
+
+| Label | Category                              | Risk Level | Description                               |
+| ----- | ------------------------------------- | ---------- | ----------------------------------------- |
+| S1    | General Political Topics              | 🟢 Low     | General political discussion              |
+| S2    | Sensitive Political Topics            | 🔴 High    | National security, subversion, separatism |
+| S3    | Insult to National Symbols or Leaders | 🔴 High    | Insults against leaders or symbols        |
+| S4    | Harm to Minors                        | 🟡 Medium  | Child abuse, exploitation, nudity         |
+| S5    | Violent Crime                         | 🔴 High    | Murder, terrorism, violent acts           |
+| S6    | Non-Violent Crime                     | 🟡 Medium  | Fraud, drugs, hacking                     |
+| S7    | Pornography                           | 🟡 Medium  | Sexual or obscene content                 |
+| S8    | Hate & Discrimination                 | 🟢 Low     | Hate speech, discrimination               |
+| S9    | Prompt Attacks                        | 🔴 High    | Jailbreaks, injections, manipulation      |
+| S10   | Profanity                             | 🟢 Low     | Vulgar or insulting language              |
+| S11   | Privacy Invasion                      | 🟢 Low     | Leakage of personal data                  |
+| S12   | Commercial Violations                 | 🟢 Low     | Fraud, trade secret leakage               |
+| S13   | Intellectual Property Infringement    | 🟢 Low     | Copyright or patent violations            |
+| S14   | Harassment                            | 🟢 Low     | Verbal abuse or targeting                 |
+| S15   | Weapons of Mass Destruction           | 🔴 High    | Nuclear, chemical, biological weapons     |
+| S16   | Self-Harm                             | 🟡 Medium  | Suicide or self-injury                    |
+| S17   | Sexual Crimes                         | 🔴 High    | Sexual assault or exploitation            |
+| S18   | Threats                               | 🟢 Low     | Threats or intimidation                   |
+| S19   | Professional Advice                   | 🟢 Low     | Medical, legal, financial advice          |
+
+---
+
+## Quick Start
+
+### Using Transformers
+
+```bash
+pip install torch transformers accelerate
+```
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "openguardrails/OpenGuardrails-Text-4B-0124"
+
+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)
+
+```bash
+vllm serve openguardrails/OpenGuardrails-Text-4B-0124 \
+  --served-model-name OpenGuardrails-Text-4B-0124 \
+  --max-model-len 8192 \
+  --port 8000
+```
+
+---
+
+### OpenAI-Compatible API
+
+```python
+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-4B-0124",
+    messages=messages,
+    temperature=0.0
+)
+
+print(result.choices[0].message.content)
+# unsafe\nS5
+```
+
+---
+
+## Output Format
+
+| Output       | Description                        |
+| ------------ | ---------------------------------- |
+| `safe`       | Content is safe                    |
+| `unsafe\nS#` | Unsafe content with category label |
+
+---
+
+## License
+
+Released under the **Apache License 2.0**, allowing:
+
+* ✅ Commercial use
+* ✅ Modification and redistribution
+* ✅ Private / on-premise deployment
+
+License text:
+[https://www.apache.org/licenses/LICENSE-2.0](https://www.apache.org/licenses/LICENSE-2.0)
+
+---
+
+## Related Resources
+
+* **Website:** [https://www.openguardrails.com](https://www.openguardrails.com)
+* **GitHub:** [https://github.com/openguardrails/openguardrails](https://github.com/openguardrails/openguardrails)
+
+---
+
+## Citation
+
+```bibtex
+@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},
+}
+```

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+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "max_position_embeddings": 262144,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 5000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.52.4",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "do_sample": true,
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+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.52.4"
+}

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special_tokens_map.json

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