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| Model | Function Calling | MMLU | GPQA | GSM-8K | MATH | MT-bench | Win | Loss | Tie | Win Rate | Loss Rate | Adjusted Win Rate |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Phi-3 Mini 128k Instruct (June) | - | 69.36 | 27.01 | 83.7 | 32.92 | 8.02 | 21 | 72 | 67 | 0.13125 | 0.45000 | 0.340625 |
| Rubra Enhanced Phi-3 Mini 128k Instruct (June) | 70.00% | 67.87 | 29.69 | 79.45 | 30.80 | 8.21 | 72 | 21 | 67 | 0.45000 | 0.13125 | 0.659375 |
| Phi-3 Mini 128k Instruct (April) | - | 68.17 | 25.90 | 80.44 | 28.12 | 7.92 | 51 | 45 | 64 | 0.31875 | 0.28125 | 0.51875 |
| Rubra Enhanced Phi-3 Mini 128k Instruct (April) | 65.71% | 66.66 | 29.24 | 74.09 | 26.84 | 7.45 | 45 | 51 | 64 | 0.28125 | 0.31875 | 0.48125 |
e2ecb24bd9dae689bb30dafcf13cbbc9dbddead5 is the last commit to have the April-based Phi-3 model. The latest in main is built off the June model@misc {rubra_ai_2024,
author = { Sanjay Nadhavajhala and Yingbei Tong },
title = { Phi-3-mini-128k-instruct },
year = 2024,
url = { https://huggingface.co/rubra-ai/Phi-3-mini-128k-instruct },
doi = { 10.57967/hf/2682 },
publisher = { Hugging Face }
}