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HRM-Text-1B

sapientinc

A 1 B-parameter language model checkpoint built on the **Hierarchical Reasoning Model (HRM)** architecture, trained by Sapient Intelligence from scratch on structured public datasets. HRM is a dual-timescale recurrent architecture: two Transformer modules (H = high-level / slow, L = low-level / fast) iterate over the same input embeddings for `H_cycles × (L_cycles + 1)` steps, with additive state injection (`z_L + z_H`). This gives effectively unbounded compute depth at bounded parameter count. This is a **pre-alignment** model checkpoint, not a chat or instruction-following assistant. It is pre-trained on a PrefixLM objective with condition prefix tokens and has **not** been multi-turn dialogue tuned, long-context adapted, instruction-tuned, RLHF-trained, or otherwise aligned for assistant-style use. If you want to use HRM-Text like a chat model, you would need to perform further alignment, such as SFT and/or RL, on task-specific data. This checkpoint is meant to serve as a starting point, not a finished assistant.

Text generation

Input Text→ Output Text

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Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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HRM-Text-1B

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