Adding Evaluation Results

README.md

@@ -114,4 +114,17 @@ unfiltered content from the internet, which is far from neutral. As the openAI t
 > not recommend that they be deployed into systems that interact with humans unless the deployers first carry out a
 > study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,
 > and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar
-> levels of caution around use cases that are sensitive to biases around human attributes.
+> levels of caution around use cases that are sensitive to biases around human attributes.
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_crumb__gpt2023)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 24.85   |
+| ARC (25-shot)         | 21.93          |
+| HellaSwag (10-shot)   | 31.11    |
+| MMLU (5-shot)         | 25.05         |
+| TruthfulQA (0-shot)   | 40.71   |
+| Winogrande (5-shot)   | 50.12   |
+| GSM8K (5-shot)        | 0.3        |
+| DROP (3-shot)         | 4.73         |