Editing Models with Task Arithmetic
Paper
• 2212.04089 • Published
• 7
Mahou-1.5-mistral-nemo-12B-lorablated
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the task arithmetic merge method using flammenai/Mahou-1.5-mistral-nemo-12B + nbeerbower/Mistral-Nemo-12B-abliterated-LORA as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
base_model: flammenai/Mahou-1.5-mistral-nemo-12B+nbeerbower/Mistral-Nemo-12B-abliterated-LORA
dtype: bfloat16
merge_method: task_arithmetic
parameters:
normalize: false
slices:
- sources:
- layer_range: [0, 40]
model: flammenai/Mahou-1.5-mistral-nemo-12B+nbeerbower/Mistral-Nemo-12B-abliterated-LORA
parameters:
weight: 1.0
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 26.45 |
| IFEval (0-Shot) | 68.25 |
| BBH (3-Shot) | 36.08 |
| MATH Lvl 5 (4-Shot) | 5.29 |
| GPQA (0-shot) | 3.91 |
| MuSR (0-shot) | 16.55 |
| MMLU-PRO (5-shot) | 28.60 |
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard68.250
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard36.080
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard5.290
- acc_norm on GPQA (0-shot)Open LLM Leaderboard3.910
- acc_norm on MuSR (0-shot)Open LLM Leaderboard16.550
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard28.600