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---
library_name: transformers
tags:
- mergekit
- merge
base_model:
- allknowingroger/Qwenslerp2-14B
- rombodawg/Rombos-LLM-V2.6-Qwen-14b
- VAGOsolutions/SauerkrautLM-v2-14b-DPO
- Qwen/Qwen2.5-14B
- CultriX/Qwen2.5-14B-Wernicke
model-index:
- name: Qwestion-14B
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: 63.18
name: strict accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CultriX/Qwestion-14B
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: 48.76
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CultriX/Qwestion-14B
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: 31.72
name: exact match
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CultriX/Qwestion-14B
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: 15.77
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CultriX/Qwestion-14B
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: 17.22
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CultriX/Qwestion-14B
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: 49.14
name: accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=CultriX/Qwestion-14B
name: Open LLM Leaderboard
license: apache-2.0
language:
- en
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 [Qwen/Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) as a base.
### Models Merged
The following models were included in the merge:
* [allknowingroger/Qwenslerp2-14B](https://huggingface.co/allknowingroger/Qwenslerp2-14B)
* [rombodawg/Rombos-LLM-V2.6-Qwen-14b](https://huggingface.co/rombodawg/Rombos-LLM-V2.6-Qwen-14b)
* [VAGOsolutions/SauerkrautLM-v2-14b-DPO](https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-DPO)
* [CultriX/Qwen2.5-14B-Wernicke](https://huggingface.co/CultriX/Qwen2.5-14B-Wernicke)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: CultriX/Qwen2.5-14B-Wernicke
parameters:
weight: 0.55 # Backbone model for conversational ability and GPQA
density: 0.80 # Retain most critical parameters for stability and strength
- model: VAGOsolutions/SauerkrautLM-v2-14b-DPO
parameters:
weight: 0.20 # High IFEval and MMLU-PRO performance with minimized weaknesses
density: 0.60 # Focus on impactful parameters for specific benchmarks
- model: rombodawg/Rombos-LLM-V2.6-Qwen-14b
parameters:
weight: 0.25 # Enhanced emphasis on reasoning-heavy tasks like MUSR and MATH
density: 0.70 # Retain reasoning-intensive parameters for improved benchmarks
- model: allknowingroger/Qwenslerp2-14B
parameters:
weight: 0.15 # General stabilizer for consistency across all tasks
density: 0.65 # Focus on balance and avoiding redundancy
base_model: Qwen/Qwen2.5-14B
merge_method: dare_ties
parameters:
normalize: true # Ensure parameter scale consistency
int8_mask: true # Optimize for memory and compute efficiency
dtype: bfloat16
tokenizer_source: Qwen/Qwen2.5-14B-Instruct
adaptive_merge_parameters:
task_weights:
IFEval: 1.0 # Maintain high IFEval performance
MATH: 1.3 # Prioritize reasoning and calculation-heavy tasks
GPQA: 1.1 # Boost factual recall and reasoning accuracy
MUSR: 1.2 # Enhance logical reasoning and factual understanding
MMLU-PRO: 1.0 # Retain consistent knowledge representation
smoothing_factor: 0.15 # Fine-tune blending for stable transitions between tasks
gradient_clipping: 1.0 # Prevent over-contribution from any single model
```
# [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__Qwestion-14B)
| Metric |Value|
|-------------------|----:|
|Avg. |37.63|
|IFEval (0-Shot) |63.18|
|BBH (3-Shot) |48.76|
|MATH Lvl 5 (4-Shot)|31.72|
|GPQA (0-shot) |15.77|
|MuSR (0-shot) |17.22|
|MMLU-PRO (5-shot) |49.14| |