Instructions to use openbmb/UltraRM-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/UltraRM-13b with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("openbmb/UltraRM-13b") model = AutoModel.from_pretrained("openbmb/UltraRM-13b") - Notebooks
- Google Colab
- Kaggle
Adding Evaluation Results
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README.md
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_openbmb__UltraRM-13b)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 25.35 |
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| ARC (25-shot) | 28.16 |
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| HellaSwag (10-shot) | 26.13 |
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| MMLU (5-shot) | 25.96 |
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| TruthfulQA (0-shot) | 47.91 |
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| Winogrande (5-shot) | 49.33 |
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| GSM8K (5-shot) | 0.0 |
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| DROP (3-shot) | 0.0 |
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