arcee-lite-GGUF

QuantFactory/arcee-lite-GGUF

This is quantized version of arcee-ai/arcee-lite created using llama.cpp

Original Model Card

<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

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.

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