Merge Models
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Models I merged using mergekit library
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Merged Model
This model is currently ranked #1 among the models up to 15B parameters and #56 among all models on the Open LLM Leaderboard.
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the SLERP merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
base_model: sometimesanotion/Lamarck-14B-v0.7
dtype: bfloat16
merge_method: slerp
parameters:
t:
- filter: self_attn
value: [0.0, 0.5, 0.3, 0.7, 1.0]
- filter: mlp
value: [1.0, 0.5, 0.7, 0.3, 0.0]
- value: 0.5
slices:
- sources:
- layer_range: [0, 48]
model: sometimesanotion/Lamarck-14B-v0.7
- layer_range: [0, 48]
model: sometimesanotion/Qwenvergence-14B-v12-Prose-DS
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 43.32 |
| IFEval (0-Shot) | 76.56 |
| BBH (3-Shot) | 50.33 |
| MATH Lvl 5 (4-Shot) | 54.00 |
| GPQA (0-shot) | 15.10 |
| MuSR (0-shot) | 16.34 |
| MMLU-PRO (5-shot) | 47.59 |
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard76.560
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard50.330
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard54.000
- acc_norm on GPQA (0-shot)Open LLM Leaderboard15.100
- acc_norm on MuSR (0-shot)Open LLM Leaderboard16.340
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard47.590