| | --- |
| | language: |
| | - en |
| | license: apache-2.0 |
| | tags: |
| | - text-generation-inference |
| | - transformers |
| | - unsloth |
| | - qwen2 |
| | - trl |
| | base_model: unsloth/qwen2.5-14b-instruct-bnb-4bit |
| | model-index: |
| | - name: Qwen2.5-Math-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: 60.66 |
| | name: strict accuracy |
| | source: |
| | url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=qingy2019/Qwen2.5-Math-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: 47.02 |
| | name: normalized accuracy |
| | source: |
| | url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=qingy2019/Qwen2.5-Math-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: 28.47 |
| | name: exact match |
| | source: |
| | url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=qingy2019/Qwen2.5-Math-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: 16.33 |
| | name: acc_norm |
| | source: |
| | url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=qingy2019/Qwen2.5-Math-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: 19.63 |
| | name: acc_norm |
| | source: |
| | url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=qingy2019/Qwen2.5-Math-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: 48.12 |
| | name: accuracy |
| | source: |
| | url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=qingy2019/Qwen2.5-Math-14B-Instruct |
| | name: Open LLM Leaderboard |
| | --- |
| | |
| | # Uploaded model |
| |
|
| | - **Developed by:** qingy2019 |
| | - **License:** apache-2.0 |
| | - **Finetuned from model :** unsloth/qwen2.5-14b-instruct-bnb-4bit |
| |
|
| | This Qwen 2.5 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. |
| |
|
| | I fine-tuned it for 400 steps on [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) with a batch size of 3. |
| |
|
| | [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
| |
|
| | # [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_qingy2019__Qwen2.5-Math-14B-Instruct) |
| |
|
| | | Metric |Value| |
| | |-------------------|----:| |
| | |Avg. |36.71| |
| | |IFEval (0-Shot) |60.66| |
| | |BBH (3-Shot) |47.02| |
| | |MATH Lvl 5 (4-Shot)|28.47| |
| | |GPQA (0-shot) |16.33| |
| | |MuSR (0-shot) |19.63| |
| | |MMLU-PRO (5-shot) |48.12| |
| |
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