A compact, no-frills research workhorse built for studying how language models develop and behave. Its defining trait is transparency — it's part of a carefully controlled suite where training was deliberately deduped to remove repeated data, making it useful for studying how data repetition affects learning. At 1B parameters with a modest 2048-token context, it's limited in reasoning depth and knowledge breadth compared to larger models.