🤝Mistral 7B-v1 Merges
Collection
Trying and comparing all merge methods on 3 Mistral-7B models
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4 items
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Updated
This is a merge of pre-trained language models created using mergekit.
This model was merged using the SLERP merge method.
The following models were included in the merge:
Since Slerp allows merging two models at a time, the following YAML configurations were used to produce this model:
slices:
- sources:
- model: HuggingFaceH4/zephyr-7b-beta
layer_range: [0, 32]
- model: NousResearch/Hermes-2-Pro-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: HuggingFaceH4/zephyr-7b-beta
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
Then
slices:
- sources:
- model: ./merge
layer_range: [0, 32]
- model: instructlab/merlinite-7b-lab
layer_range: [0, 32]
merge_method: slerp
base_model: ./merge
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
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 67.76 |
AI2 Reasoning Challenge (25-Shot) | 64.25 |
HellaSwag (10-Shot) | 85.47 |
MMLU (5-Shot) | 64.89 |
TruthfulQA (0-shot) | 53.57 |
Winogrande (5-shot) | 79.16 |
GSM8k (5-shot) | 59.21 |