QuantFactory/walledguard-c-GGUF
QuantFactory/walledguard-c-GGUF
This is quantized version of 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 admin@walled.ai
- 🔥 Please check out our LLM Safety Evaluation One-Stop Center: **Walled Eval**!
<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 [admin@walled.ai](mailto:admin@walled.ai).</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
Direct Use
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;">▲ 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;">▲ 9.4%</span></td> </tr> </tbody> </table>
Table: Scores on DynamoBench, 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**!
Model Citation
TO BE ADDED