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
QuantFactory/Tulu-3.1-8B-SuperNova-GGUF
This is quantized version of bunnycore/Tulu-3.1-8B-SuperNova created using llama.cpp
Original Model Card
merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the linear merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
models:
- model: arcee-ai/Llama-3.1-SuperNova-Lite
parameters:
weight: 1.0
- model: allenai/Llama-3.1-Tulu-3-8B
parameters:
weight: 1.0
- model: meditsolutions/Llama-3.1-MedIT-SUN-8B
parameters:
weight: 1.0
merge_method: linear
normalize: false
int8_mask: true
dtype: bfloat16
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 30.94 |
| IFEval (0-Shot) | 81.94 |
| BBH (3-Shot) | 32.50 |
| MATH Lvl 5 (4-Shot) | 24.32 |
| GPQA (0-shot) | 6.94 |
| MuSR (0-shot) | 8.69 |
| MMLU-PRO (5-shot) | 31.27 |
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard81.940
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard32.500
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard24.320
- acc_norm on GPQA (0-shot)Open LLM Leaderboard6.940
- acc_norm on MuSR (0-shot)Open LLM Leaderboard8.690
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard31.270