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--- |
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library_name: transformers |
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tags: |
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- mergekit |
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- merge |
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base_model: |
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- bunnycore/QwQen-3B-LCoT |
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- bunnycore/Qwen-2.5-3b-R1-lora_model-v.1 |
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model-index: |
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- name: QwQen-3B-LCoT-R1 |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: IFEval (0-Shot) |
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type: HuggingFaceH4/ifeval |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: inst_level_strict_acc and prompt_level_strict_acc |
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value: 53.42 |
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name: strict accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/QwQen-3B-LCoT-R1 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: BBH (3-Shot) |
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type: BBH |
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args: |
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num_few_shot: 3 |
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metrics: |
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- type: acc_norm |
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value: 26.98 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/QwQen-3B-LCoT-R1 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MATH Lvl 5 (4-Shot) |
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type: hendrycks/competition_math |
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args: |
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num_few_shot: 4 |
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metrics: |
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- type: exact_match |
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value: 33.53 |
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name: exact match |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/QwQen-3B-LCoT-R1 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GPQA (0-shot) |
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type: Idavidrein/gpqa |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: acc_norm |
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value: 1.57 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/QwQen-3B-LCoT-R1 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MuSR (0-shot) |
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type: TAUR-Lab/MuSR |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: acc_norm |
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value: 10.03 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/QwQen-3B-LCoT-R1 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU-PRO (5-shot) |
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type: TIGER-Lab/MMLU-Pro |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 30.26 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/QwQen-3B-LCoT-R1 |
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name: Open LLM Leaderboard |
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--- |
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When using the QwQen-3B-LCoT-R1 model, you might notice that it can sometimes produce repetitive outputs, especially in certain contexts or with specific prompts. This is a common behavior in language models, but don’t worry—it can be managed effectively by tweaking the model’s repetition parameters. |
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### To reduce repetition, you can experiment with the following settings: |
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- Repetition Penalty: This parameter discourages the model from repeating the same words or phrases by applying a penalty. A higher value (e.g., 1.0) will push the model to avoid repetition more aggressively. |
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- Temperature: This controls the randomness of the output. Lowering the temperature (e.g., 0.7) makes the model more focused and less likely to repeat itself, though it may reduce creativity slightly. |
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## System Prompt: |
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``` |
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Think about the reasoning process in the mind first, then provide the answer. |
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The reasoning process should be wrapped within <think> </think> tags, then provide the answer after that, i.e., <think> reasoning process here </think> answer here. |
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``` |
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### Configuration |
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The following YAML configuration was used to produce this model: |
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```yaml |
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base_model: bunnycore/QwQen-3B-LCoT+bunnycore/Qwen-2.5-3b-R1-lora_model-v.1 |
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dtype: bfloat16 |
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merge_method: passthrough |
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models: |
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- model: bunnycore/QwQen-3B-LCoT+bunnycore/Qwen-2.5-3b-R1-lora_model-v.1 |
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tokenizer_source: bunnycore/QwQen-3B-LCoT |
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``` |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/bunnycore__QwQen-3B-LCoT-R1-details) |
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| Metric |Value| |
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|-------------------|----:| |
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|Avg. |25.97| |
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|IFEval (0-Shot) |53.42| |
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|BBH (3-Shot) |26.98| |
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|MATH Lvl 5 (4-Shot)|33.53| |
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|GPQA (0-shot) | 1.57| |
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|MuSR (0-shot) |10.03| |
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|MMLU-PRO (5-shot) |30.26| |
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