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 using huihui-ai/Llama-3.2-3B-Instruct-abliterated as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
merge_method: linear
dtype: bfloat16
normalize: true
base_model: huihui-ai/Llama-3.2-3B-Instruct-abliterated
models:
- model: bunnycore/Llama-3.2-3B-All-Mix
parameters:
weight: 10
density: 1
- model: prithivMLmods/Codepy-Deepthink-3B
parameters:
weight: 7
density: 0.8
- model: huihui-ai/Llama-3.2-3B-Instruct-abliterated
parameters:
weight: 10
density: 1
- model: HuggingFaceTB/finemath-ablation-infiwebmath
parameters:
weight: 7
density: 0.8
- model: prithivMLmods/Llama-Sentient-3.2-3B-Instruct
parameters:
weight: 7
density: 0.8
- model: passing2961/Thanos-3B
parameters:
weight: 7
density: 0.8
- model: bunnycore/Llama-3.2-3B-RP-DeepThink
parameters:
weight: 7
density: 0.8
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 22.47 |
| IFEval (0-Shot) | 66.79 |
| BBH (3-Shot) | 23.04 |
| MATH Lvl 5 (4-Shot) | 13.52 |
| GPQA (0-shot) | 3.58 |
| MuSR (0-shot) | 3.15 |
| MMLU-PRO (5-shot) | 24.76 |