walledai/walledguard-c

WalledGuard

• 🔥 WalledGuard comes in two versions: Community and Advanced*. To get access to the advanced version, please contact us at [email protected]

• 🔥 Please check out our LLM Safety Evaluation One-Stop Center: Walled Eval!

  (*More performant, suitable for enterprise use)

Note: We also provide customized guardrails for enterprise-specific use cases, please reach out to us at [email protected].
Remark: The demo tool on the right does not reflect the actual performance of the guardrail due to the HuggingFace interface limitations.

Model Details

Model Description

• 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

Model	DynamoBench	XSTest	P-Safety	R-Safety	Average Scores
Llama Guard 1	77.67	85.33	71.28	86.13	80.10
Llama Guard 2	82.67	87.78	79.69	89.64	84.95
Llama Guard 3	83.00	88.67	80.99	89.58	85.56
WalledGuard-C (Community Version)	92.00	86.89	87.35	86.78	88.26 ▲ 3.2%
WalledGuard-A (Advanced Version)	92.33	96.44	90.52	90.46	92.94 ▲ 8.1%

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!

Citation

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

@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

[email protected]