Qwen2.5-14B
Collection
CultriX Qwen2.5-14B Models.
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5 items
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Updated
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1
merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the DARE TIES merge method using CultriX/Qwen2.5-14B-MegaMerge-pt1 as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
# final_dare_ties_merge.yaml
models:
- model: CultriX/Qwen2.5-14B-MergeStock
parameters:
density: 0.5 # Retain 50% of the most significant parameters
weight: 0.6 # Emphasize MergeStock's contributions
- model: CultriX/Qwen2.5-14B-Wernicke
parameters:
density: 0.5 # Retain 50% of the most significant parameters
weight: 0.4 # Incorporate Wernicke's contributions
merge_method: dare_ties
base_model: CultriX/Qwen2.5-14B-MegaMerge-pt1
parameters:
normalize: true
int8_mask: true
dtype: bfloat16
tokenizer_source: Qwen/Qwen2.5-14B-Instruct
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 36.69 |
| IFEval (0-Shot) | 56.83 |
| BBH (3-Shot) | 50.91 |
| MATH Lvl 5 (4-Shot) | 27.34 |
| GPQA (0-shot) | 17.23 |
| MuSR (0-shot) | 18.74 |
| MMLU-PRO (5-shot) | 49.12 |
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard52.350
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard50.640
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard30.060
- acc_norm on GPQA (0-shot)Open LLM Leaderboard19.130
- acc_norm on MuSR (0-shot)Open LLM Leaderboard18.250
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard49.150