Mirendil Releases Its First From-Scratch Pretrained Model, Fern

Oct 27

1. 20:57

   MarkTechPost

   Mirendil Releases Its First Model Fern 0.1, Supporting a 10M Token Context

   Mirendil has released its first model, Fern 0.1, a coding model trained from scratch with a context window of up to 10M tokens, equivalent to roughly 23 MB of code, or about 500,000 lines. Mirendil-Base was pretrained by a single researcher in 3.5 months, with a total compute cost of less than $10 million; AI agents wrote more than 99% of the code, and the training data came only from public sources. RULER single-needle retrieval scored 100% at every length from 32K to 10M tokens, but Terminal Bench 4.0 scored only 6.1%, and SWE-Bench Verified scored 73.4%. Currently only hosted early access is available, and the weights have not been open-sourced.

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2. 11:05

   Shao Meng

   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.

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