This model is a merge of my personal favourite models, i couldn't decide between them so why not have both? Without MOE cause gpu poor :3
With my own tests it gives kuro-lotus like results without the requirement for a highly detailed character card and stays coherent when roping up to 8K context.
I personally use the "Universal Light" preset in silly tavern, with "alpaca" the results can be short but are longer with "alpaca roleplay".
"Universal Light" preset can be extremely creative but sometimes likes to act for user with some cards, for those i like just the "default" but any preset seems to work!
merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the SLERP merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: saishf/Kuro-Lotus-10.7B
layer_range: [0, 48]
- model: Sao10K/Fimbulvetr-10.7B-v1
layer_range: [0, 48]
merge_method: slerp
base_model: saishf/Kuro-Lotus-10.7B
parameters:
t:
- filter: self_attn
value: [0.6, 0.7, 0.8, 0.9, 1]
- filter: mlp
value: [0.4, 0.3, 0.2, 0.1, 0]
- value: 0.5
dtype: bfloat16
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 72.73 |
AI2 Reasoning Challenge (25-Shot) | 69.54 |
HellaSwag (10-Shot) | 87.87 |
MMLU (5-Shot) | 66.99 |
TruthfulQA (0-shot) | 60.95 |
Winogrande (5-shot) | 84.14 |
GSM8k (5-shot) | 66.87 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard69.540
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard87.870
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard66.990
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard60.950
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard84.140
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard66.870