Sapient HRM-Text – a 1B PoC text gen model based on the HRM architecture

Sapient Inc. open-sourced HRM-Text in May 2026, a 1.15 billion parameter text generation model based on the HRM architecture. Trained on roughly 40 billion tokens—up to 1,000 times less data than comparable models—it achieves competitive scores on reasoning benchmarks including 56.2% on MATH and 82.2% on DROP. The proof-of-concept model runs locally with a 0.6 GiB footprint at int4 quantization, enabling advanced reasoning without cloud dependency.

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.