MonarchLake-7B
This model equips AlphaMonarch-7B with a strong base of emotional intelligence.
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: mlabonne/AlphaMonarch-7B
layer_range: [0, 32]
- model: macadeliccc/WestLake-7b-v2-laser-truthy-dpo
layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/AlphaMonarch-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 76.10 |
AI2 Reasoning Challenge (25-Shot) | 74.15 |
HellaSwag (10-Shot) | 89.29 |
MMLU (5-Shot) | 64.44 |
TruthfulQA (0-shot) | 74.97 |
Winogrande (5-shot) | 85.48 |
GSM8k (5-shot) | 68.31 |
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Model tree for macadeliccc/MonarchLake-7B
Base model
mlabonne/Monarch-7B
Finetuned
mlabonne/NeuralMonarch-7B
Finetuned
mlabonne/AlphaMonarch-7B
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard74.150
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard89.290
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.440
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard74.970
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard85.480
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard68.310