Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Paper
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2203.05482
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Published
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7
This is a merge of pre-trained language models created using mergekit.
This model was merged using the Linear merge method.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview
parameters:
weight: 1.0
- model: gz987/qwen2.5-7b-cabs-v0.3
parameters:
weight: 1.0
- model: suayptalha/Clarus-7B-v0.2
parameters:
weight: 1.0
- model: suayptalha/Clarus-7B-v0.2+bunnycore/Qwen-2.5-7b-rp-lora
parameters:
weight: 1.0
- model: suayptalha/Clarus-7B-v0.2+bunnycore/Qwen-2.5-7b-rp-lora
parameters:
weight: 1.0
- model: gz987/qwen2.5-7b-cabs-v0.3+bunnycore/Qwen-2.5-7b-s1k-lora_model
parameters:
weight: 1.0
merge_method: linear
normalize: false
int8_mask: true
dtype: bfloat16
tokenizer_source: "union" # or "base" or a model path