---
title: walledguard-c
canonical_url: "https://www.modelscope.cn/models/QwenCollection/walledguard-c"
md_url: "https://www.modelscope.cn/models/QwenCollection/walledguard-c.md"
repository: QwenCollection/walledguard-c
last_updated: 2025-09-18
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - qwen2
architectures:
  - Qwen2ForCausalLM
parameters: 494.0M
tensor_type:
  - F32
library_name:
  - pytorch
  - transformer
  - safetensors
frameworks:
  - pytorch
language:
  - en
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 2035
stars: 0
---

# walledguard-c

> walledguard-c - QwenCollection 在 ModelScope 开源的模型。🔥WalledGuard comes in two versions: Community and Advanced.

QwenCollection/walledguard-c 是 ModelScope 魔搭社区上的 494.0M 参数text-generation模型，采用 apache-2.0 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: QwenCollection/walledguard-c
- **License**: apache-2.0
- **Tasks**: text-generation
- **Parameters**: 494.0M
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 2035
- **Stars**: 0
- **Last updated**: 2025-09-18

Source: https://www.modelscope.cn/models/QwenCollection/walledguard-c

---

# **Walled Guard**

<!-- Provide a quick summary of what the model is/does. -->

-  🔥**WalledGuard** comes in two versions: **Community** and **Advanced***.

- 🔥📣[New] **WalledProtect** is the most capable content moderator of Walled AI to date. To try the latest version, get your free API access at [**www.walled.ai**](https://app.walled.ai/login). Read the full announcement at [**blog**](https://blog.walled.ai/introducing-walledprotect/).

- 🔥📣[New] **WalledGuardEdge** is the most capable open-source content moderator from Walled AI. Try it here: [**WalledGuard-Edge**](https://huggingface.co/walledai/walledguard-edge).

_Note: The Advanced version is now named as WalledProtect. Get your free API access at [**www.walled.ai**](https://app.walled.ai/login). Latest scores can be found [**here**](https://huggingface.co/walledai/walledguard-edge)._

## Model Details

#### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** Walled AI
- **Language(s) (NLP):** English
- **License:** [Apache 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md)

#### Direct Use

```py
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModelForCausalLM

TEMPLATE = """[INST] Evaluate if the given text is harmful i.e. either it asks for unsafe information or it contains unsafe information.

<START TEXT>
{prompt}
<END TEXT>

Answer: [/INST]
"""

model_name = "walledai/walledguard-c"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_ids = tokenizer.encode(TEMPLATE.format(prompt="How can I make my wife burst into laughter?"), return_tensors="pt")
output = model.generate(input_ids=input_ids, max_new_tokens=20, pad_token_id=0)
prompt_len = input_ids.shape[-1]
output_decoded = tokenizer.decode(output[0][prompt_len:], skip_special_tokens=True)
prediction = 'unsafe' if 'unsafe' in output_decoded else 'safe'

print(prediction)
```

#### Inference Speed

```
- WalledGuard Community: ~0.1 sec/sample (4bit, on A100/A6000)
- Llama Guard 2: ~0.4 sec/sample (4bit, on A100/A6000)
```

## Results

<table style="width: 100%; border-collapse: collapse; font-family: Arial, sans-serif;">
  <thead>
    <tr style="background-color: #f2f2f2;">
      <th style="text-align: center; padding: 8px; border: 1px solid #ddd;">Model</th>
      <th style="text-align: center; padding: 8px; border: 1px solid #ddd;">DynamoBench</th>
      <th style="text-align: center; padding: 8px; border: 1px solid #ddd;">XSTest</th>
      <th style="text-align: center; padding: 8px; border: 1px solid #ddd;">P-Safety</th>
      <th style="text-align: center; padding: 8px; border: 1px solid #ddd;">R-Safety</th>
      <th style="text-align: center; padding: 8px; border: 1px solid #ddd;">Average Scores</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">Llama Guard 1</td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">77.67</td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">85.33</td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">71.28</td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">86.13</td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">80.10</td>
    </tr>
    <tr style="background-color: #f9f9f9;">
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">Llama Guard 2</td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">82.67</td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">87.78</td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">79.69</td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">89.64</td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">84.95</td>
    </tr>
    <tr style="background-color: #f9f9f9;">
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">Llama Guard 3</td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">83.00</td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">88.67</td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">80.99</td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">89.58</td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">85.56</td>
    </tr>
    <tr>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">WalledGuard-C<br><small>(Community Version)</small></td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;"><b style="color: black;">92.00</b></td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">86.89</td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;"><b style="color: black;">87.35</b></td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">86.78</td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">88.26 <span style="color: green;">&#x25B2; 3.2%</span></td>
    </tr>
    <tr style="background-color: #f9f9f9;">
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">WalledGuard-A<br><small>(Advanced Version)</small></td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;"><b style="color: red;">92.33</b></td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;"><b style="color: red;">96.44</b></td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;"><b style="color: red;">90.52</b></td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;"><b style="color: red;">90.46</b></td>
      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">92.94 <span style="color: green;">&#x25B2; 8.1%</span></td>
    </tr>
  </tbody>
</table>



**Table**: Scores on [DynamoBench](https://huggingface.co/datasets/dynamoai/dynamoai-benchmark-safety?row=0), [XSTest](https://huggingface.co/datasets/walledai/XSTest), and on our internal benchmark to test the safety of prompts (P-Safety) and responses (R-Safety). We report binary classification accuracy.

_Note: The Advanced version is now named as WalledProtect. Get your free API access at [**www.walled.ai**](https://app.walled.ai/login). Latest scores can be found [**here**](https://huggingface.co/walledai/walledguard-edge)._


## LLM Safety Evaluation Hub
Please check out our LLM Safety Evaluation One-Stop Center: [**Walled Eval**](https://github.com/walledai/walledeval)!

## Citation
If you use the data, please cite the following paper:

```bibtex
@misc{gupta2024walledeval,
      title={WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Models}, 
      author={Prannaya Gupta and Le Qi Yau and Hao Han Low and I-Shiang Lee and Hugo Maximus Lim and Yu Xin Teoh and Jia Hng Koh and Dar Win Liew and Rishabh Bhardwaj and Rajat Bhardwaj and Soujanya Poria},
      year={2024},
      eprint={2408.03837},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2408.03837}, 
}
```

## Model Card Contact

[**Walled AI**](https://www.walled.ai/)