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: FuseAI/FuseChat-Qwen-2.5-7B-Instruct
parameters:
weight: 0.5
- model: prithivMLmods/QwQ-LCoT-7B-Instruct
parameters:
weight: 1.0
- model: fblgit/cybertron-v4-qw7B-UNAMGS
parameters:
weight: 0.3
- model: fblgit/cybertron-v4-qw7B-UNAMGS+bunnycore/Qwen-2.1-7b-Persona-lora_model
parameters:
weight: 0.6
merge_method: linear
normalize: false
int8_mask: true
dtype: bfloat16
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 29.40 |
| IFEval (0-Shot) | 72.75 |
| BBH (3-Shot) | 35.91 |
| MATH Lvl 5 (4-Shot) | 12.01 |
| GPQA (0-shot) | 5.93 |
| MuSR (0-shot) | 11.98 |
| MMLU-PRO (5-shot) | 37.85 |
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 34.68 |
| IFEval (0-Shot) | 72.75 |
| BBH (3-Shot) | 35.91 |
| MATH Lvl 5 (4-Shot) | 43.66 |
| GPQA (0-shot) | 5.93 |
| MuSR (0-shot) | 11.98 |
| MMLU-PRO (5-shot) | 37.85 |