---
license: apache-2.0
---
<div align="center">
  <img src="https://i.ibb.co/g9Z2CGQ/arcee-lite.webp" alt="Arcee-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>


Arcee-Lite is a compact yet powerful 1.5B parameter language model developed as part of the DistillKit open-source project. Despite its small size, Arcee-Lite demonstrates impressive performance, particularly in the MMLU (Massive Multitask Language Understanding) benchmark.

## GGUFS available [here](https://huggingface.co/arcee-ai/arcee-lite-GGUF)

## Key Features

- **Model Size**: 1.5 billion parameters
- **MMLU Score**: 55.93
- **Distillation Source**: Phi-3-Medium
- **Enhanced Performance**: Merged with high-performing distillations

## About DistillKit

DistillKit is our new open-source project focused on creating efficient, smaller models that maintain high performance. Arcee-Lite is one of the first models to emerge from this initiative.

## Performance

Arcee-Lite showcases remarkable capabilities for its size:

- Achieves a 55.93 score on the MMLU benchmark
- Demonstrates exceptional performance across various tasks

## Use Cases

Arcee-Lite is suitable for a wide range of applications where a balance between model size and performance is crucial:

- Embedded systems
- Mobile applications
- Edge computing
- Resource-constrained environments

<div align="center">
  <img src="https://i.ibb.co/hDC7WBt/Screenshot-2024-08-01-at-8-59-33-AM.png" alt="Arcee-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>

Please note that our internal evaluations were consistantly higher than their counterparts on the OpenLLM Leaderboard - and should only be compared against the relative performance between the models, not weighed against the leaderboard.

---