Hermes-3-Llama-3.2-3B-GGUF
| Property | Value |
|---|---|
| Parameter Count | 3 Billion |
| Base Model | Llama-3.2 |
| Developer | Nous Research |
| Format | GGUF Quantized |
| Paper | arXiv:2408.11857 |
What is Hermes-3-Llama-3.2-3B-GGUF?
Hermes-3-Llama-3.2-3B-GGUF is a quantized version of Nous Research's latest addition to their Hermes series. It's a full parameter fine-tune of the Llama-3.2 3B foundation model, specifically designed to align with user needs while providing powerful steering capabilities and control. The model demonstrates competitive performance against Llama-3.1 Instruct models, achieving an average score of 64% on GPT4All benchmarks.
Implementation Details
The model utilizes the ChatML prompt format, enabling structured multi-turn dialogue and system-level instructions. It supports advanced features like function calling and structured JSON outputs, making it particularly suitable for practical applications.
- Trained on H100s using LambdaLabs GPU Cloud
- Implements ChatML format for enhanced conversation control
- Supports both standard chat and function calling capabilities
- Available in various GGUF quantized formats for efficient deployment
Core Capabilities
- Advanced agentic capabilities and improved reasoning
- Enhanced multi-turn conversation handling
- Better long context coherence
- Powerful function calling and structured output capabilities
- Improved code generation skills
- Strong performance on standardized benchmarks
Frequently Asked Questions
Q: What makes this model unique?
Hermes 3 3B stands out for its combination of small size and powerful capabilities, particularly in areas like roleplaying, reasoning, and multi-turn conversations. It's the first Nous Research fine-tune in the 3B parameter class, offering competitive performance against larger models.
Q: What are the recommended use cases?
The model is well-suited for general assistant tasks, structured output generation, function calling applications, and scenarios requiring detailed reasoning or multi-turn conversations. Its GGUF format makes it particularly suitable for deployment in resource-constrained environments.
