Adding Evaluation Results

README.md

@@ -115,4 +115,17 @@ See evaluations for the main models and detailed ablations in Section 3 and safe
 
 Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.
 
-Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-use-guide](https://ai.meta.com/llama/responsible-use-guide).
+Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-use-guide](https://ai.meta.com/llama/responsible-use-guide).
+# [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_codellama__CodeLlama-13b-hf)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 37.91   |
+| ARC (25-shot)         | 40.87          |
+| HellaSwag (10-shot)   | 63.35    |
+| MMLU (5-shot)         | 32.81         |
+| TruthfulQA (0-shot)   | 43.79   |
+| Winogrande (5-shot)   | 67.17   |
+| GSM8K (5-shot)        | 12.13        |
+| DROP (3-shot)         | 5.25         |