Hermes 4 - Llama-3.1 405B is a large-scale, instruction-tuned language model developed by Nous Research. Built upon Meta's Llama 3.1 405B base, it represents the flagship variant of the Hermes 4 family. The model is defined by a neutral alignment philosophy, designed to prioritize user-directed instructions and system prompts over external moralizing filters, which typically results in lower refusal rates and higher steerability compared to standard frontier models.
The model features a hybrid reasoning architecture that allows it to operate in two distinct modes. In its non-reasoning or standard mode, the model provides direct, high-speed responses for general-purpose tasks, roleplay, and creative writing. Alternatively, the model can be prompted to enter a reasoning mode where it generates internal deliberation traces within <think> tags to solve complex mathematical, coding, and logical problems.
Training for Hermes 4 utilized a post-training corpus of approximately 60 billion tokens, a fifty-fold increase over the previous Hermes 3 generation. This dataset was curated using the DataForge synthetic data pipeline and Atropos rejection sampling, focusing on expanding the model's capabilities in STEM and logical reasoning. The model maintains a 128k-token context window, enabling it to handle long-form documents and extended multi-turn conversations without significant loss of context.