CHISEI-1.5B-R0

Abstract

CHISEI-1.5B-R0 investigates methods for maximizing general-purpose capability within an approximately 1.5-billion-parameter language model. The study proceeds from the premise that the relationship between parameter count and capability is not linear — that significant capability can be unlocked through deliberate architectural choices, curated training data, and systematic optimization, rather than through scale alone. The project name derives from the Japanese word for intelligence (知性, chisei), reflecting the program's commitment to studying the nature of capability itself rather than its quantity. CHISEI-1.5B-R0 represents the initial revision of this experimental line; findings will establish a reproducible baseline for subsequent compact-model studies.

Objectives

1. 01.

   Develop a reproducible compact-model training methodology that can serve as a reference standard for future Intelligence Density research.

2. 02.

   Establish an Intelligence Density evaluation framework enabling rigorous measurement of capability per unit of computational budget.

3. 03.

   Measure capability gains against compute-matched controls to isolate the contribution of architectural and methodological choices.

4. 04.

   Validate practical deployment on accessible hardware to ensure findings are transferable beyond laboratory conditions.