genevaquant/Hermes-4-70B
Hermes 4 70B is a 70 billion parameter, Llama-3.1-based reasoning model developed by Nous Research. It features a hybrid reasoning mode with explicit deliberation segments and is optimized for math, code, STEM, logic, and creative writing. The model also excels at schema adherence and producing valid structured outputs like JSON, making it suitable for complex reasoning tasks and reliable data generation.
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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 on Llama-3.1. It introduces a hybrid reasoning mode that allows the model to deliberate internally using <think>...</think> segments before generating a response, significantly enhancing its problem-solving capabilities. This model was trained on a substantially increased post-training corpus of approximately 5 million samples and 60 billion tokens, blending reasoning and non-reasoning data.
Key Capabilities
- Enhanced Reasoning: Demonstrates massive improvements across math, code, STEM, logic, and creative writing tasks.
- Schema Adherence & Structured Outputs: Specifically trained to produce valid JSON for given schemas and to repair malformed objects, ensuring reliable structured data generation.
- Steerability & Alignment: Offers significant improvements in steerability and reduced refusal rates, aiming for greater alignment with user values.
- Function Calling & Tool Use: Supports function/tool calls within a single assistant turn, produced after its internal reasoning process.
Good for
- Applications requiring advanced reasoning in technical and creative domains.
- Generating reliable structured data (e.g., JSON) that adheres to specific schemas.
- Use cases where model steerability and reduced refusals are critical.
- Integrating tool use and function calling into AI workflows for complex tasks.