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As the rapid adoption of chat bots and QandA models continues, so do the concerns for their reliability and safety. In response to this, many state-of-the-art models are being tuned to act as Safety Guardrails to protect against malicious usage and avoid undesired, harmful output. I published a Hugging Face blog introducing a simple, proof-of-concept, RoBERTa-based LLM that my team and I finetuned to detect toxic prompt inputs into chat-style LLMs. The article explores some of the tradeoffs of fine-tuning larger decoder vs. smaller encoder models and asks the question if "simpler is better" in the arena of toxic prompt detection.
As the rapid adoption of chat bots and QandA models continues, so do the concerns for their reliability and safety. In response to this, many state-of-the-art models are being tuned to act as Safety Guardrails to protect against malicious usage and avoid undesired, harmful output. I published a Hugging Face blog introducing a simple, proof-of-concept, RoBERTa-based LLM that my team and I finetuned to detect toxic prompt inputs into chat-style LLMs. The article explores some of the tradeoffs of fine-tuning larger decoder vs. smaller encoder models and asks the question if "simpler is better" in the arena of toxic prompt detection.
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🔗 to blog:  
🔗 to blog:  
link
https://huggingface.co/blog/daniel-de-leon/toxic-prompt-roberta
https://huggingface.co/blog/daniel-de-leon/toxic-prompt-roberta
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🔗 to model: 
🔗 to model: 
resource
https://huggingface.co/Intel/toxic-prompt-roberta
model
Intel/toxic-prompt-roberta
https://huggingface.co/Intel/toxic-prompt-roberta
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🔗 to OPEA microservice: 
🔗 to OPEA microservice: 
link
https://github.com/opea-project/GenAIComps/tree/main/comps/guardrails/toxicity_detection
https://github.com/opea-project/GenAIComps/tree/main/comps/guardrails/toxicity_detection
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A huge thank you to my colleagues that helped contribute: 
A huge thank you to my colleagues that helped contribute: 
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@qgao007
qgao007
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, 
, 
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@mitalipo
mitalipo
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, 
, 
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@ashahba
ashahba
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 and Fahim Mohammad
 and Fahim Mohammad
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As the rapid adoption of chat bots and QandA models continues, so do the concerns for their reliability and safety. In response to this, many state-of-the-art models are being tuned to act as Safety Guardrails to protect against malicious usage and avoid undesired, harmful output. I published a Hugging Face blog introducing a simple, proof-of-concept, RoBERTa-based LLM that my team and I finetuned to detect toxic prompt inputs into chat-style LLMs. The article explores some of the tradeoffs of fine-tuning larger decoder vs. smaller encoder models and asks the question if "simpler is better" in the arena of toxic prompt detection.

🔗 to blog:  https://huggingface.co/blog/daniel-de-leon/toxic-prompt-roberta
🔗 to model: https://huggingface.co/Intel/toxic-prompt-roberta
🔗 to OPEA microservice: https://github.com/opea-project/GenAIComps/tree/main/comps/guardrails/toxicity_detection

A huge thank you to my colleagues that helped contribute: @qgao007, @mitalipo, @ashahba and Fahim Mohammad

🔥
TuringsSolutions ImranzamanML qgao007 mitalipo PetarMiladinov lunarflu darkzbaron
👀
John6666