qingy2024/Qwen2.5-Ultimate-14B-Instruct

---

library_name: transformers

tags:

• mergekit

• merge

base_model:

• Qwen/Qwen2.5-14B-Instruct

• qingy2019/Qwen2.5-Math-14B-Instruct

• Qwen/Qwen2.5-14B

language:

• zho

• eng

• fra

• spa

• por

• deu

• ita

• rus

• jpn

• kor

• vie

• tha

• ara

model-index:

• name: Qwen2.5-Ultimate-14B-Instruct

  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: 39.38

      name: strict accuracy

    source:

    url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=qingy2019/Qwen2.5-Ultimate-14B-Instruct

    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: 40.58

      name: normalized accuracy

    source:

    url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=qingy2019/Qwen2.5-Ultimate-14B-Instruct

    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: 28.02

      name: exact match

    source:

    url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=qingy2019/Qwen2.5-Ultimate-14B-Instruct

    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: 14.21

      name: acc_norm

    source:

    url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=qingy2019/Qwen2.5-Ultimate-14B-Instruct

    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: 9.89

      name: acc_norm

    source:

    url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=qingy2019/Qwen2.5-Ultimate-14B-Instruct

    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: 43.66

      name: accuracy

    source:

    url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=qingy2019/Qwen2.5-Ultimate-14B-Instruct

    name: Open LLM Leaderboard

---

Qwen2.5 Ultimate 14B Instruct

Merged using rombodawg's method and using the first iteration of my Qwen2.5 Math 14B Instruct.

Merge Details

Merge Method

This model was merged using the TIES merge method using Qwen/Qwen2.5-14B as a base.

Models Merged

The following models were included in the merge:

• Qwen/Qwen2.5-14B-Instruct

• qingy2019/Qwen2.5-Math-14B-Instruct

Configuration

The following YAML configuration was used to produce this model:

models:

  - model: qingy2019/Qwen2.5-Math-14B-Instruct

    parameters:

      weight: 1

      density: 1

  - model: Qwen/Qwen2.5-14B-Instruct

    parameters:

      weight: 1

      density: 1

merge_method: ties

base_model: Qwen/Qwen2.5-14B

parameters:

  weight: 1

  density: 1

  normalize: true

  int8_mask: true

tokenizer_source: qingy2019/Qwen2.5-Math-14B-Instruct

dtype: bfloat16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

| Metric |Value|

|-------------------|----:|

|Avg. |29.29|

|IFEval (0-Shot) |39.38|

|BBH (3-Shot) |40.58|

|MATH Lvl 5 (4-Shot)|28.02|

|GPQA (0-shot) |14.21|

|MuSR (0-shot) | 9.89|

|MMLU-PRO (5-shot) |43.66|