README.md

---
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|