# Sapient HRM-Text – a 1B PoC text gen model based on the HRM architecture > Source: > Published: 2026-06-05 06:15:46+00:00 # HRM-Text Open-sourced in May 2026, HRM-Text is a 1B text generation model based on the HRM architecture, strengthened by task completion and latent space reasoning. [Download HRM-Text](https://github.com/sapientinc/HRM-Text) ## Key Traits #### Data-Efficient Training Trained on ~40B tokens, using up to 1000× less data than the 4–36T tokens used by the models we benchmark against. #### Compact Yet Powerful Built with 1.15B parameters while remaining competitive with models several times its size on reasoning-heavy benchmarks. #### Native Edge Reasoning Runs locally with a 0.6 GiB footprint at int4 quantization, enabling advanced reasoning without cloud dependency. ## Application Domains Our architecture powers advanced reasoning across complex, high-impact real-world domains. ## Benchmarks HRM-Text is a proof-of-concept model with no post-training. The numbers below reflect architecture performance alone. ### MATH ### DROP ### ARC-C ### MMLU Despite its compact size, HRM-Text delivers competitive results across reasoning benchmarks, including 56.2% on MATH, 81.9% on ARC-Challenge, 82.2% on DROP, and 60.7% on MMLU. ## Benchmark Explanations #### MATH: A benchmark that tests mathematical reasoning and problem solving, often requiring multi-step logic rather than simple recall. #### ARC-C: The AI2 Reasoning Challenge- Challenge Set, designed to test science reasoning through difficult grade-school science questions that require inference and commonsense understanding. #### DROP: A reading comprehension benchmark that tests a model’s ability to reason over passages, especially with numbers, counting, comparison, and discrete operations. #### MMLU Massive Multitask Language Understanding, a broad benchmark covering many subjects, used to evaluate general knowledge and multi-domain reasoning.