Mirendil Releases Fern, Its First Model Pretrained from Scratch, Supporting 10M Token Context

Mirendil released Fern, its first model pretrained from scratch, with a context of up to 10M tokens, equivalent to roughly 500,000 lines of code (Megatron-LM scale) or several days of an agent's work trajectory. The first version, Fern 0.1, underwent only a short round of post-training; its architecture, training, and serving were all designed for a 10M context, and next-token metrics continue to improve as context length grows. It targets high-throughput work such as auditing data, reading training logs, and reading through agent trajectories, and lists six scenarios including work on larger repositories, agentic coding, AI research code, and long-log debugging.