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Maxime Labonne shares that their model is trending on Hugging Face and is surprisingly capable at agentic tasks despite having only 1B active parameters.
HRM-text is a 1B-parameter hierarchical reasoning language model proposed by Sapient Intelligence. It thinks efficiently through internal latent space, achieving performance surpassing most models of the same size with extremely low training cost.
HRM-Text is a 1B parameter text generation model that uses a brain-inspired hierarchical recurrent architecture to achieve efficient pretraining with only 40B tokens and ~$1000, enabling accessible foundation model training with dramatically reduced compute and data requirements.
Sapient Intelligence introduces HRM-Text, a 1B-parameter reasoning language model trained on only 40B tokens with a budget of $1,000, achieving competitive performance while drastically reducing data and compute requirements.
Sapient Intelligence released HRM-Text-1B, a 1-billion-parameter language model with a novel dual-timescale recurrent architecture (Hierarchical Reasoning Model) that provides unbounded compute depth at bounded parameter count. The pre-alignment checkpoint is available on Hugging Face.