The Hermes 4 - Llama-3.1 70B (Non-reasoning) model is a high-performance fine-tuned version of Meta's Llama-3.1-70B architecture, developed by Nous Research. This specific variant refers to the standard instruction-following execution mode of the Hermes 4 family, optimized for direct responses and low-latency interaction. Unlike the "reasoning" or "thinking" modes of the same family, the non-reasoning version bypasses explicit chain-of-thought processing to deliver fast, intuitive outputs for general-purpose assistant tasks and high-throughput workflows.
Built using a massive post-training corpus of approximately 5 million samples and 60 billion tokens, Hermes 4 represents a significant scale-up from its predecessor, Hermes 3. The training dataset emphasizes verified reasoning traces, leading to improved performance in coding, mathematics, STEM, and logical analysis. A core design philosophy of the series is its commitment to neutral alignment and user steerability, allowing the model to follow complex instructions without the excessive refusal rates or safety filters often found in proprietary systems.
Technically, the model supports a 128k token context window (131,072 tokens), enabling it to handle large document sets and extended multi-turn conversations. It features robust capabilities for structured data generation, including precise JSON schema adherence and reliable function calling. On benchmarks such as RefusalBench, the Hermes 4 series has demonstrated a high degree of helpfulness and adherence to user values while maintaining state-of-the-art performance for an open-weight model of its size class.