Model Stock: All we need is just a few fine-tuned models
Paper
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2403.19522
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Published
•
13
This is a merge of pre-trained language models created using mergekit.
This model was merged using the Model Stock merge method using google-bert/bert-large-uncased as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
out_dtype: float32
merge_method: model_stock
base_model: google-bert/bert-large-uncased
models:
- model: cointegrated/roberta-large-cola-krishna2020
parameters:
weight: 0.42
- model: albert/albert-large-v2
parameters:
weight: 0.35
- model: OpenAssistant/reward-model-deberta-v3-large-v2
parameters:
weight: 1.0
- model: google-bert/bert-large-cased
parameters:
weight: 0.24
- model: klue/roberta-large
parameters:
weight: 0.32
- model: jiayi1129/spanbert-large-squad
parameters:
weight: 1.1
parameters:
normalize: true
int8_mask: false