Adding Evaluation Results

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  1. README.md +14 -1
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@@ -133,4 +133,17 @@ The code in this project is open-source under the [Apache-2.0](LICENSE) license,
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  ## Acknowledgements
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  - In this project, we used model weights from BigScience's [bloomz-7b1-mt](https://huggingface.co/bigscience/bloomz-7b1-mt) and Meta's [Llama 2](https://huggingface.co/meta-llama) series as initialization weights, along with vocabulary expansion.
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  - The training code in this project was inspired by Databricks' [dolly](https://github.com/databrickslabs/dolly) project and Huggingface's [transformers](https://github.com/huggingface/transformers) library.
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- - Distributed training in this project utilized Microsoft's [DeepSpeed](https://github.com/microsoft/deepspeed) distributed training tool and configuration files from Huggingface transformers' [ZeRO stage 2](https://huggingface.co/docs/transformers/main_classes/deepspeed#zero2-config).
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Acknowledgements
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  - In this project, we used model weights from BigScience's [bloomz-7b1-mt](https://huggingface.co/bigscience/bloomz-7b1-mt) and Meta's [Llama 2](https://huggingface.co/meta-llama) series as initialization weights, along with vocabulary expansion.
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  - The training code in this project was inspired by Databricks' [dolly](https://github.com/databrickslabs/dolly) project and Huggingface's [transformers](https://github.com/huggingface/transformers) library.
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+ - Distributed training in this project utilized Microsoft's [DeepSpeed](https://github.com/microsoft/deepspeed) distributed training tool and configuration files from Huggingface transformers' [ZeRO stage 2](https://huggingface.co/docs/transformers/main_classes/deepspeed#zero2-config).
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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_wenge-research__yayi-7b)
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+
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+ | Metric | Value |
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+ |-----------------------|---------------------------|
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+ | Avg. | 37.99 |
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+ | ARC (25-shot) | 46.33 |
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+ | HellaSwag (10-shot) | 61.72 |
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+ | MMLU (5-shot) | 36.34 |
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+ | TruthfulQA (0-shot) | 43.7 |
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+ | Winogrande (5-shot) | 62.27 |
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+ | GSM8K (5-shot) | 0.91 |
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+ | DROP (3-shot) | 14.67 |