Q2.5-R1-7B / README.md
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Adding Evaluation Results (#1)
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metadata
license: apache-2.0
library_name: transformers
tags:
  - mergekit
  - merge
base_model:
  - unsloth/Qwen2.5-7B-Instruct
  - nbeerbower/R1-Qwen-7B-LORA
model-index:
  - name: Q2.5-R1-7B
    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: 13.46
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Triangle104/Q2.5-R1-7B
          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: 2.55
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Triangle104/Q2.5-R1-7B
          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: 1.66
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Triangle104/Q2.5-R1-7B
          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: 0.34
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Triangle104/Q2.5-R1-7B
          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: 2.69
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Triangle104/Q2.5-R1-7B
          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: 2
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Triangle104/Q2.5-R1-7B
          name: Open LLM Leaderboard

Merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Qwen2.5-7B-Instruct tied with Reasoning LORA.

Merge Method

This model was merged using the Passthrough merge method using unsloth/Qwen2.5-7B-Instruct + nbeerbower/R1-Qwen-7B-LORA as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: unsloth/Qwen2.5-7B-Instruct+nbeerbower/R1-Qwen-7B-LORA
dtype: float16
merge_method: passthrough
models:
  - model: unsloth/Qwen2.5-7B-Instruct+nbeerbower/R1-Qwen-7B-LORA
tokenizer_source: unsloth/Qwen2.5-7B

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 3.78
IFEval (0-Shot) 13.46
BBH (3-Shot) 2.55
MATH Lvl 5 (4-Shot) 1.66
GPQA (0-shot) 0.34
MuSR (0-shot) 2.69
MMLU-PRO (5-shot) 2.00