README.md

---
language:
- en
license: llama3
library_name: transformers
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
datasets:
- arcee-ai/EvolKit-20k
---
<div align="center">
  <img src="https://i.ibb.co/r072p7j/eopi-ZVu-SQ0-G-Cav78-Byq-Tg.png" alt="Llama-3.1-SuperNova-Lite" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;">
</div>

## Overview

Llama-3.1-SuperNova-Lite is an 8B parameter model developed by Arcee.ai, based on the Llama-3.1-8B-Instruct architecture. It is a distilled version of the larger Llama-3.1-405B-Instruct model, leveraging offline logits extracted from the 405B parameter variant. This 8B variation of Llama-3.1-SuperNova maintains high performance while offering exceptional instruction-following capabilities and domain-specific adaptability. 

The model was trained using a state-of-the-art distillation pipeline and an instruction dataset generated with [EvolKit](https://github.com/arcee-ai/EvolKit), ensuring accuracy and efficiency across a wide range of tasks. For more information on its training, visit blog.arcee.ai. 

Llama-3.1-SuperNova-Lite excels in both benchmark performance and real-world applications, providing the power of large-scale models in a more compact, efficient form ideal for organizations seeking high performance with reduced resource requirements.

# Evaluations
Here are our internal benchmarks using the main branch of lm evaluation harness:

| Benchmark   | SuperNova-Lite | Llama-3.1-8b-Instruct |
|-------------|----------------|----------------------|
| IF_Eval     | 81.1           | 77.4                 |
| MMLU Pro    | 38.7           | 37.7                 |
| TruthfulQA  | 64.4           | 55.0                 |
| BBH         | 51.1           | 50.6                 |
| GPQA        | 31.2           | 29.02                |

The script used for evaluation can be found inside this repository under /eval.sh, or click [here](https://huggingface.co/arcee-ai/Llama-3.1-SuperNova-Lite/blob/main/eval).