Built with Axolotl

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%
Downloads last month
22
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Datasets used to train LoneStriker/loyal-piano-m7-4.0bpw-h6-exl2