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---
base_model:
- CultriX/Qwen2.5-14B-MegaMerge-pt1
- CultriX/Qwen2.5-14B-Wernicke
- CultriX/Qwen2.5-14B-MergeStock
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
language:
- en
model-index:
- name: Qwen2.5-14B-MegaMerge-pt2
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 52.35
name: strict accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CultriX/Qwen2.5-14B-MegaMerge-pt2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 50.64
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CultriX/Qwen2.5-14B-MegaMerge-pt2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 30.06
name: exact match
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CultriX/Qwen2.5-14B-MegaMerge-pt2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 19.13
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CultriX/Qwen2.5-14B-MegaMerge-pt2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 18.25
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CultriX/Qwen2.5-14B-MegaMerge-pt2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 49.15
name: accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CultriX/Qwen2.5-14B-MegaMerge-pt2
name: Open LLM Leaderboard
metrics:
- accuracy
pipeline_tag: text-generation
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [CultriX/Qwen2.5-14B-MegaMerge-pt1](https://huggingface.co/CultriX/Qwen2.5-14B-MegaMerge-pt1) as a base.
### Models Merged
The following models were included in the merge:
* [CultriX/Qwen2.5-14B-Wernicke](https://huggingface.co/CultriX/Qwen2.5-14B-Wernicke)
* [CultriX/Qwen2.5-14B-MergeStock](https://huggingface.co/CultriX/Qwen2.5-14B-MergeStock)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
# 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](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_CultriX__Qwen2.5-14B-MegaMerge-pt2)
| 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 | |