# Trinity-Nano (6B)

**Overview**

Trinity Nano is a 6B-parameter (1B active) sparse mixture-of-experts language model, optimized for high-efficiency inference in real-time, on-device, and embedded AI applications.

**Key Features**

* Efficient attention mechanism: reduces memory and compute requirements while preserving long-context coherence.
* 128K-token context window: supports multi-turn interactions and extended document processing.
* Strong context utilization: fully leverages long inputs for coherent multi-turn reasoning and reliable function/tool calls.
* High inference efficiency: generates tokens rapidly while minimizing compute, delivering an outstanding price-to-performance ratio.

### Deployment Quickstart

To get started deploying Trinity-Nano, download the model [here](https://huggingface.co/arcee-ai) and proceed to [quick-deploys](https://docs.arcee.ai/quick-deploys "mention")

### Model Summary

|                                  |                                                                                                  |
| -------------------------------- | ------------------------------------------------------------------------------------------------ |
| Name                             | Trinity-Nano-6B                                                                                  |
| Architecture                     | Mixture-of-Experts                                                                               |
| Parameters                       | 6 Billion Total, 1 Billion Active                                                                |
| Experts                          | 128 Experts, 8 Active                                                                            |
| Attention Mechanism              | Grouped Query Attention (GQA)                                                                    |
| Training Tokens                  | 10 trillion                                                                                      |
| License                          | Apache 2.0                                                                                       |
| Recommended Inference Parameters | <ul><li>temperature: 0.15</li><li>top\_p: 0.75</li><li>top\_k: 50</li><li>min\_p: 0.06</li></ul> |