DeepHermes-3-Llama-3-3B-Preview-GGUF
| Property | Value |
|---|---|
| Base Model | Llama-3 3B |
| Quantized Versions | Q4 (1.8GB), Q5 (2.2GB), Q6 (2.5GB), Q8 (3.2GB) |
| Authors | NousResearch |
| Model URL | huggingface.co/NousResearch/DeepHermes-3-Llama-3-3B-Preview-GGUF |
What is DeepHermes-3-Llama-3-3B-Preview-GGUF?
DeepHermes-3 Preview represents a groundbreaking advancement in language models, uniquely combining traditional LLM responses with sophisticated reasoning capabilities in a single 3B parameter model. It's one of the first models to successfully integrate both intuitive responses and long chain-of-thought reasoning, controlled through system prompts.
Implementation Details
The model is available in multiple GGUF quantized versions for efficient inference, ranging from 1.8GB to 3.2GB in size. It supports both the Llama.cpp framework and vLLM for deployment, with comprehensive function calling capabilities and structured JSON output modes.
- Supports Flash Attention 2 for improved performance
- Uses Llama-Chat format for structured multi-turn dialogue
- Includes specialized system prompts for deep reasoning mode
- Features advanced function calling with XML-based tool integration
Core Capabilities
- Dual-mode operation: traditional chat and deep reasoning
- Enhanced agentic capabilities and roleplaying
- Improved multi-turn conversation handling
- Long context coherence
- Structured JSON output generation
- Function calling with detailed API integration
Frequently Asked Questions
Q: What makes this model unique?
DeepHermes-3 stands out for its ability to switch between traditional chat responses and deep reasoning modes through system prompts, making it highly versatile for different use cases. Its reasoning capabilities can utilize up to 13,000 tokens for complex problem-solving.
Q: What are the recommended use cases?
The model excels in scenarios requiring both quick responses and deep analytical thinking, including complex problem-solving, API integration through function calling, structured data output via JSON mode, and engaging multi-turn conversations. It's particularly suited for applications needing flexible reasoning capabilities.
