Instructions to use Menouar/saqr-7b-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Menouar/saqr-7b-beta with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Menouar/saqr-7b-merged") model = PeftModel.from_pretrained(base_model, "Menouar/saqr-7b-beta") - Notebooks
- Google Colab
- Kaggle
Adding Evaluation Results
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by leaderboard-pr-bot - opened
README.md
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@@ -55,4 +55,17 @@ The following hyperparameters were used during training:
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- Transformers 4.38.0.dev0
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- Pytorch 2.1.0+cu121
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- Datasets 2.17.0
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- Tokenizers 0.15.2
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- Transformers 4.38.0.dev0
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- Pytorch 2.1.0+cu121
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- Datasets 2.17.0
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- Tokenizers 0.15.2
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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_Menouar__saqr-7b-beta)
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| Metric |Value|
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|Avg. |44.84|
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|AI2 Reasoning Challenge (25-Shot)|47.78|
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|HellaSwag (10-Shot) |77.61|
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|MMLU (5-Shot) |25.80|
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|TruthfulQA (0-shot) |39.38|
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|Winogrande (5-shot) |70.56|
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|GSM8k (5-shot) | 7.88|
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