Hermes 4 - Llama-3.1 70B is a large language model developed by Nous Research, built upon the architecture of Meta's Llama 3.1 70B. It is distinguished as a hybrid-mode reasoning model, which integrates structured, multi-step deliberation with general instruction-following capabilities. The model is trained to engage in an internal "thinking" process, often enclosed within <think> tags, allowing it to solve complex mathematical, scientific, and logical problems by breaking them down into discrete steps.
The training process for Hermes 4 utilized an extensively expanded post-training corpus of approximately 5 million samples (~60 billion tokens), representing a significant increase in data volume over previous versions. This dataset incorporates high-quality synthetic data and verified reasoning traces, aiming to enhance the model's accuracy in coding, STEM subjects, and roleplay while preserving general assistant performance.
Hermes 4 follows a neutral alignment philosophy, designed to be highly steerable and responsive to user prompts with minimal content filtering. Technical features include robust schema adherence for structured JSON outputs and the ability to repair malformed data objects. The model maintains a context window of 131,072 tokens, enabling the analysis of long documents and sustained multi-turn conversations.