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license: apache-2.0
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<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;">
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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;">
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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.
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