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| license: apache-2.0 |
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| # QuantFactory/arcee-lite-GGUF |
| This is quantized version of [arcee-ai/arcee-lite](https://huggingface.co/arcee-ai/arcee-lite) created using llama.cpp |
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| # Original Model Card |
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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. |
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| ## GGUFS available [here](https://huggingface.co/arcee-ai/arcee-lite-GGUF) |
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| ## Key Features |
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| - **Model Size**: 1.5 billion parameters |
| - **MMLU Score**: 55.93 |
| - **Distillation Source**: Phi-3-Medium |
| - **Enhanced Performance**: Merged with high-performing distillations |
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| ## About DistillKit |
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| 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. |
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| ## Performance |
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| Arcee-Lite showcases remarkable capabilities for its size: |
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| - Achieves a 55.93 score on the MMLU benchmark |
| - Demonstrates exceptional performance across various tasks |
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| ## Use Cases |
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| Arcee-Lite is suitable for a wide range of applications where a balance between model size and performance is crucial: |
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| - Embedded systems |
| - Mobile applications |
| - Edge computing |
| - Resource-constrained environments |
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| <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> |
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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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