NousResearch/Hermes-4-70B

Hugging Face
TEXT GENERATIONConcurrency Cost:4Model Size:70BQuant:FP8Ctx Length:32kTool Calling:SupportedPublished:Aug 18, 2025License:llama3Architecture:Transformer0.2K Warm

NousResearch/Hermes-4-70B is a 70 billion parameter, Llama-3.1-based reasoning model developed by Nous Research, featuring a 32768 token context length. It is specifically designed for enhanced reasoning across math, code, STEM, and logic, alongside improved schema adherence and structured output generation. This model excels at being steerable and aligned to user values, demonstrating state-of-the-art performance on RefusalBench.

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Hermes 4 70B: A Frontier Reasoning Model

Hermes 4 70B, developed by Nous Research, is a 70 billion parameter model built upon Llama-3.1, designed with a focus on advanced reasoning capabilities and user alignment. It features a significantly expanded post-training corpus of approximately 5 million samples and 60 billion tokens, a substantial increase from its predecessor, Hermes 3.

Key Capabilities & Innovations

  • Hybrid Reasoning Mode: Incorporates explicit <think>…</think> segments for internal deliberation, allowing for deeper problem consideration and improved solution accuracy. Users can also opt for faster responses when deliberation is not required.
  • Enhanced Reasoning: Demonstrates massive improvements in math, code, STEM, and logic tasks, alongside better creative writing and subjective responses.
  • Schema Adherence & Structured Outputs: Trained to produce valid JSON outputs according to specified schemas and to repair malformed objects, crucial for reliable tool use and function calling.
  • Steerability & Alignment: Achieves significant improvements in steerability, with reduced refusal rates and state-of-the-art performance on RefusalBench, a benchmark designed to test models' willingness to be helpful and conform to user values without censorship.
  • Function Calling & Tool Use: Supports robust function/tool calls within a single assistant turn, integrating seamlessly with its reasoning process. Tool definitions can be provided in system messages or via a dedicated 'tools:' field.

Ideal Use Cases

  • Complex Problem Solving: Excellent for tasks requiring deep reasoning in technical domains like mathematics, coding, and scientific inquiry.
  • Structured Data Generation: Suited for applications needing precise JSON or other structured outputs, such as API interactions or data processing.
  • Customizable AI Assistants: Its high steerability and alignment capabilities make it ideal for creating personalized AI experiences that adhere to specific user preferences and ethical guidelines.
  • Advanced Chatbots: Can power chatbots that require sophisticated reasoning, nuanced responses, and the ability to handle complex instructions.