DynaGuard-1.7B is a 1.7 billion parameter decoder-only Transformer model developed by the University of Maryland and Capital One, based on Qwen3-1.7B. This guardian model evaluates text against user-defined natural language policies, offering a flexible solution for moderating chatbot outputs beyond static harm categories. It excels at enforcing bespoke, application-specific rules and provides both fast inference and interpretable Chain-of-Thought reasoning modes. DynaGuard-1.7B achieves strong performance on safety and compliance benchmarks, making it suitable for dynamic content moderation.
Loading preview...
Model tree for
tomg-group-umd/DynaGuard-1.7BMost commonly used values from Featherless users
temperature
This setting influences the sampling randomness. Lower values make the model more deterministic; higher values introduce randomness. Zero is greedy sampling.
top_p
This setting controls the cumulative probability of considered top tokens. Must be in (0, 1]. Set to 1 to consider all tokens.
top_k
This limits the number of top tokens to consider. Set to -1 to consider all tokens.
frequency_penalty
This setting penalizes new tokens based on their frequency in the generated text. Values > 0 encourage new tokens; < 0 encourages repetition.
presence_penalty
This setting penalizes new tokens based on their presence in the generated text so far. Values > 0 encourage new tokens; < 0 encourages repetition.
repetition_penalty
This setting penalizes new tokens based on their appearance in the prompt and generated text. Values > 1 encourage new tokens; < 1 encourages repetition.
min_p
This setting representing the minimum probability for a token to be considered relative to the most likely token. Must be in [0, 1]. Set to 0 to disable.