OPEN-1B makes every training step independently auditable

Gensyn releases OPEN-1B, a 1.61B-parameter model trained on 400B tokens with a state hash for every optimizer step. Its fixed-order kernels, data stream and topology-independent reductions reproduce the same bits across CPU, CUDA and Metal. The release includes the full corpus, 810 checkpoints, training code and an audit-replay harness. A volunteer can verify one step on commodity hardware instead of rerunning the full training job.

For teams evaluating open models, this adds a verifiable tier above published weights and recipes. It can expose undeclared data or training changes without requiring an auditor to fund a full retraining run. The 1B scale and custom deterministic kernels make it a research prototype rather than a drop-in recipe for frontier training.