merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the TIES merge method using gz987/qwen2.5-7b-cabs-v0.3 as a base.
Models Merged
The following models were included in the merge:
- rombodawg/Rombos-LLM-V2.5-Qwen-7b
- suayptalha/Clarus-7B-v0.1
- prithivMLmods/WebMind-7B-v0.1
- fblgit/cybertron-v4-qw7B-MGS
- Xiaojian9992024/Qwen2.5-THREADRIPPER-Small
Configuration
The following YAML configuration was used to produce this model:
models:
- model: gz987/qwen2.5-7b-cabs-v0.3
#no parameters necessary for base model
- model: suayptalha/Clarus-7B-v0.1
parameters:
density: 0.2
weight: 0.2
- model: Xiaojian9992024/Qwen2.5-THREADRIPPER-Small
parameters:
density: 0.2
weight: 0.2
- model: rombodawg/Rombos-LLM-V2.5-Qwen-7b
parameters:
density: 0.2
weight: 0.2
- model: prithivMLmods/WebMind-7B-v0.1
parameters:
density: 0.2
weight: 0.2
- model: fblgit/cybertron-v4-qw7B-MGS
parameters:
density: 0.2
weight: 0.2
merge_method: ties
base_model: gz987/qwen2.5-7b-cabs-v0.3
parameters:
normalize: false
int8_mask: true
dtype: bfloat16
Open LLM Leaderboard Evaluation Results
Detailed results can be found here! Summarized results can be found here!
Metric | Value (%) |
---|---|
Average | 37.30 |
IFEval (0-Shot) | 76.40 |
BBH (3-Shot) | 36.62 |
MATH Lvl 5 (4-Shot) | 48.79 |
GPQA (0-shot) | 8.95 |
MuSR (0-shot) | 15.51 |
MMLU-PRO (5-shot) | 37.51 |
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Evaluation results
- averaged accuracy on IFEval (0-Shot)Open LLM Leaderboard76.400
- normalized accuracy on BBH (3-Shot)test set Open LLM Leaderboard36.620
- exact match on MATH Lvl 5 (4-Shot)test set Open LLM Leaderboard48.790
- acc_norm on GPQA (0-shot)Open LLM Leaderboard8.950
- acc_norm on MuSR (0-shot)Open LLM Leaderboard15.510
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard37.510