Walled Guard
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🔥WalledGuard comes in two versions: Community and Advanced*.
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🔥📣[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. Read the full announcement at blog.
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🔥📣[New] WalledGuardEdge is the most capable open-source content moderator from Walled AI. Try it here: WalledGuard-Edge.
Note: The Advanced version is now named as WalledProtect. Get your free API access at www.walled.ai. Latest scores can be found here.
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
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.
Note: The Advanced version is now named as WalledProtect. Get your free API access at www.walled.ai. Latest scores can be found here.
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},
}