Update README.md

README.md

@@ -55,34 +55,13 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 I encourage you to provide feedback on the model's performance. If you'd like to create your own quantizations, feel free to do so and let me know how it works for you!
 
-metrics:
-  - name: "Average ⬆️"
-    dataset_name: "Average"
-    metric_type: "average_score"
-    value: 19.46
-  - name: "IFEval"
-    dataset_name: "IFEval"
-    metric_type: "inst_level_strict_acc, prompt_level_strict_acc"
-    value: 28.06
-  - name: "BBH"
-    dataset_name: "BBH"
-    metric_type: "acc_norm"
-    value: 31.81
-  - name: "MATH Lvl 5"
-    dataset_name: "MATH Lvl 5"
-    metric_type: "exact_match"
-    value: 9.67
-  - name: "GPQA"
-    dataset_name: "GPQA"
-    metric_type: "acc_norm"
-    value: 8.5
-  - name: "MUSR"
-    dataset_name: "MuSR"
-    metric_type: "acc_norm"
-    value: 11.38
-  - name: "MMLU-PRO"
-    dataset_name: "MMLU-PRO"
-    metric_type: "acc"
-    value: 27.34
-
+| Metric       | Dataset Name  | Metric Type                         | Value  |
+|--------------|---------------|-------------------------------------|--------|
+| Average ⬆️    | Average       | average_score                       | 19.46  |
+| IFEval       | IFEval        | inst_level_strict_acc, prompt_level_strict_acc | 28.06  |
+| BBH          | BBH           | acc_norm                            | 31.81  |
+| MATH Lvl 5   | MATH Lvl 5    | exact_match                         | 9.67   |
+| GPQA         | GPQA          | acc_norm                            | 8.5    |
+| MUSR         | MuSR          | acc_norm                            | 11.38  |
+| MMLU-PRO     | MMLU-PRO      | acc                                 | 27.34  |