genevaquant/Hermes-4-14B

Hermes 4 14B: A Frontier Reasoning Model

Hermes 4 14B, developed by Nous Research, is a 14 billion parameter model built upon the Qwen 3 architecture. It introduces a hybrid reasoning mode that allows the model to deliberate internally using <think>…</think> segments before generating a response, enhancing the quality of its outputs across various domains.

Key Differentiators and Capabilities

• Vastly Expanded Training Data: The post-training corpus has been scaled up from 1M samples/1.2B tokens to approximately 5M samples and 60B tokens, blending reasoning and non-reasoning data for comprehensive improvement.
• Enhanced Reasoning: Demonstrates significant advancements in math, code, STEM, logic, and creative writing, producing high-quality, expressive results.
• Schema Adherence & Structured Outputs: Specifically trained to generate valid JSON according to provided schemas and to repair malformed objects, making it suitable for structured data tasks.
• Improved Steerability: Offers extreme improvements in steerability, notably reducing refusal rates and allowing for better alignment with user values.
• Function Calling & Tool Use: Supports robust function/tool calls within a single assistant turn, integrating them after its internal reasoning process. It uses <tool_call>{...}</tool_call> tags for easy parsing.

Use Cases

Hermes 4 14B is ideal for applications requiring:

• Complex problem-solving in technical fields like math, coding, and STEM.
• Generating structured data such as JSON outputs based on schemas.
• Creative content generation that benefits from deeper reasoning.
• Highly steerable AI assistants that can be aligned to specific user preferences and reduce unwanted refusals.