Create README.md

README.md

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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;">
+</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.
+
+## 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:
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+- 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:
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+- 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.
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