CHILLGuard: Towards Fine-Grained Chinese LLM Safety Guardrail with Scalable Data Construction and Model-aware Preference Alignment (Findings of EMNLP 2026) CHILLGuard-8B is a fine-grained Chinese LLM content-safety guardrail classifier fine-tuned from Qwen3-8B. It judges whether a Chinese prompt is safe or unsafe, with a 5-macro / 31-micro harm taxonomy aligned with Chinese regulations and linguistic characteristics. - Achieves state-of-the-art performance on Chinese content-safety benchmarks (see the paper for detailed evaluation results). The model is trained with the generator–classifier collaborative training framework via Model-aware Direct Preference Optimization (MDPO) described in…
Open weights
cc-by-nc-4.0
8.2B parameters
40,960 tokens
transformers
CHILLGuard: Towards Fine-Grained Chinese LLM Safety Guardrail with Scalable Data Construction and Model-aware Preference Alignment (Findings of EMNLP 2026) CHILLGuard-4B is a fine-grained Chinese LLM content-safety guardrail classifier fine-tuned from Qwen3-4B. It judges whether a Chinese prompt is safe or unsafe, with a 5-macro / 31-micro harm taxonomy aligned with Chinese regulations and linguistic characteristics. - Achieves state-of-the-art performance on Chinese content-safety benchmarks (see the paper for detailed evaluation results). The model is trained with the generator–classifier collaborative training framework via Model-aware Direct Preference Optimization (MDPO) described in…
Open weights
cc-by-nc-4.0
4B parameters
262,144 tokens
transformers
CHILLGuard: Towards Fine-Grained Chinese LLM Safety Guardrail with Scalable Data Construction and Model-aware Preference Alignment (Findings of EMNLP 2026) CHILLGuard-1.7B is a fine-grained Chinese LLM content-safety guardrail classifier fine-tuned from Qwen3-1.7B. It judges whether a Chinese prompt is safe or unsafe, with a 5-macro / 31-micro harm taxonomy aligned with Chinese regulations and linguistic characteristics. - Achieves state-of-the-art performance on Chinese content-safety benchmarks (see the paper for detailed evaluation results). The model is trained with the generator–classifier collaborative training framework via Model-aware Direct Preference Optimization (MDPO) described…
Open weights
cc-by-nc-4.0
2B parameters
40,960 tokens
transformers
