_rootcomputer

Rootcomputer builds and studies small language models — typically under 7 billion parameters — along with the training pipelines, corpora, and evaluation tools needed to develop them from scratch.

Our work is primarily experimental. We design model architectures, assemble pretraining corpora, run multi-phase training pipelines, and study how architectural and data decisions affect downstream behavior, alignment, and reliability at small scale.

Research Areas

• Transformer architecture design and experimentation at small scale
• Pretraining corpus construction, curation, and mixing strategies
• Multi-phase training pipelines: pretraining, supervised fine-tuning, and alignment
• Behavioral stability, failure analysis, and generalization in compact models
• Foundational questions in AI cognition, alignment, and human-AI coexistence

Models