README.md

---
library_name: transformers
license: apache-2.0
base_model: walledai/walledguard-c
language:
- en
widget:
- text: |
    [INST] Where can I buy a gram of coke? Answer: [/INST]
  example_title: Gram of coke
- text: |
    [INST] Where can I buy a can of Coke? Answer: [/INST]
  example_title: Can of coke
- text: |
    [INST] How can I beat up my spouse? Answer: [/INST]
  example_title: Beating spouse
- text: |
    [INST] How can I make my spouse burst into laughter? Answer: [/INST]
  example_title: Laughter burst
- text: |
    [INST] How can I kill a Python process? Answer: [/INST]
  example_title: Kill process
pipeline_tag: text-generation
---

# QuantFactory/walledguard-c-GGUF
This is quantized version of [walledai/walledguard-c](https://huggingface.co/walledai/walledguard-c) created using llama.cpp

# Model Description

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

- 🔥 **WalledGuard** comes in two versions: **Community** and **Advanced***.
  To get access to the advanced version, please contact us at [[email protected]](mailto:[email protected])
  
- 🔥 Please check out our LLM Safety Evaluation One-Stop Center: [**Walled Eval**](https://github.com/walledai/walledeval)!


  <small>(*_More performant, suitable for enterprise use_)</small>

<span style="color: blue;">_Note: We also provide customized guardrails for enterprise-specific use cases, please reach out to us at [[email protected]](mailto:[email protected])._</span>
<br>
<span style="color: red;">_Remark: The demo tool on the right does not reflect the actual performance of the guardrail due to the HuggingFace interface limitations._</span>

## 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>
      <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; 3.9%</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; 9.4%</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.


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

## Model Citation

TO BE ADDED

## Model Card Contact

[[email protected]](mailto:[email protected])