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--- |
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license: apache-2.0 |
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library_name: transformers |
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model-index: |
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- name: 2_PRYMMAL-ECE-7B-SLERP-V2 |
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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: 10.73 |
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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=Lil-R/2_PRYMMAL-ECE-7B-SLERP-V2 |
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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: 2.78 |
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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=Lil-R/2_PRYMMAL-ECE-7B-SLERP-V2 |
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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: 0.0 |
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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=Lil-R/2_PRYMMAL-ECE-7B-SLERP-V2 |
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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: 0.11 |
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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=Lil-R/2_PRYMMAL-ECE-7B-SLERP-V2 |
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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: 7.32 |
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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=Lil-R/2_PRYMMAL-ECE-7B-SLERP-V2 |
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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: 1.37 |
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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=Lil-R/2_PRYMMAL-ECE-7B-SLERP-V2 |
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name: Open LLM Leaderboard |
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--- |
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# Model Card for Model ID |
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## Model Details |
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### Model Description |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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## Bias, Risks, and Limitations |
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### Recommendations |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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## Training Details |
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### Training Data |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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## Evaluation |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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#### Factors |
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[More Information Needed] |
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#### Metrics |
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### Results |
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#### Summary |
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## Model Examination [optional] |
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## Environmental Impact |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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**BibTeX:** |
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## More Information [optional] |
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## Model Card Contact |
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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/details_Lil-R__2_PRYMMAL-ECE-7B-SLERP-V2) |
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| Metric |Value| |
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|-------------------|----:| |
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|Avg. | 3.72| |
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|IFEval (0-Shot) |10.73| |
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|BBH (3-Shot) | 2.78| |
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|MATH Lvl 5 (4-Shot)| 0.00| |
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|GPQA (0-shot) | 0.11| |
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|MuSR (0-shot) | 7.32| |
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|MMLU-PRO (5-shot) | 1.37| |
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