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Experimenting with dataset ratios. Intended to be a roleplay-focused model with some smarts and good long-context recall.

Not sure if I've succeeded on the roleplay front, but something sure went right! Currently the #4 7B model on the leaderboard as of 11/30/2023. Going to riff on this and see where it goes.

model Average ARC HellaSwag MMLU TruthfulQA Winogrande GSM8K DROP
fblgit/juanako-7b-UNA 59.91 68.17 85.34 62.47 65.13 78.85 20.7 38.74
Intel/neural-chat-7b-v3-1 59.06 66.21 83.64 62.37 59.65 78.14 19.56 43.84
Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-1-7B 58.6 66.55 84.47 63.34 61.22 78.37 23.58 32.66
chargoddard/loyal-piano-m7 58.42 66.72 85.03 64.43 60.03 79.08 25.7 27.92
Gryphe/MythoMist7b 58.26 65.87 83.55 62.32 59.98 78.06 20.24 37.82

Dataset composition:

dataset rows used percent of total
PIPPA 14.6k 43%
summarize_from_feedback 9k 26%
orca_mini_v1_dataset 5.6k 17%
rpguild 2.86k 8%
LimaRP 2k 6%
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Datasets used to train LoneStriker/loyal-piano-m7-8.0bpw-h8-exl2