DeepHermes-3-Mistral-24B-Preview
| Property | Value |
|---|---|
| Base Model | Mistral 24B |
| Developer | Nous Research |
| Model Type | Hybrid Reasoning LLM |
| GitHub | Hermes Function Calling |
What is DeepHermes-3-Mistral-24B-Preview?
DeepHermes-3-Mistral-24B-Preview represents a significant advancement in language model technology, being one of the first models to successfully integrate both traditional response modes and deep reasoning capabilities within a single architecture. Built on the Mistral 24B foundation, this model introduces a unique approach to AI reasoning by allowing users to toggle between intuitive responses and detailed chains of thought through specific system prompts.
Implementation Details
The model implements the Llama-Chat format for structured dialogue and features advanced capabilities in function calling and JSON output formatting. It supports both standard inference and vLLM deployment, with comprehensive support for quantized versions through GGUF formats.
- Supports flash attention 2 for optimized performance
- Implements systematic reasoning processes with <think> tags
- Features specialized function calling with structured XML-based tool calls
- Provides JSON mode for structured outputs
Core Capabilities
- Hybrid reasoning system with toggleable deep thinking mode
- Advanced agentic capabilities and improved roleplaying
- Enhanced multi-turn conversation handling
- Superior long context coherence
- Structured function calling with API integration
- JSON output formatting for structured data responses
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its ability to unify both intuitive responses and systematic reasoning within a single model, controlled through system prompts. This makes it exceptionally versatile for both quick responses and deep analytical tasks.
Q: What are the recommended use cases?
DeepHermes-3 excels in scenarios requiring complex reasoning, function calling, structured data output, and multi-turn conversations. It's particularly suitable for applications needing both quick responses and detailed analytical thinking, such as technical analysis, problem-solving, and API-integrated applications.
