Update README.md

README.md

@@ -29,31 +29,18 @@ The only significant improvement was with TruthfulQA.
 
 ## MT-bench Performance
 
-Epoch 4.5 result:
-```
-Mode: single
-Input file: data/mt_bench/model_judgment/gpt-4_single.jsonl
-
-########## First turn ##########
-                  score
-model      turn
-oo-phi-1_5 1     6.0375
-
-########## Second turn ##########
-                 score
-model      turn
-oo-phi-1_5 2     4.025
-
-########## Average ##########
-              score
-model
-oo-phi-1_5  5.03125
-```
+| Epoch     | Average   | Turn 1    | Turn 2    |
+|:----------|:----------|:----------|:----------|
+| 3         | 4.85      | 5.69      | 4.01      |
+| 3.5       | 5.19      | 5.91      | 4.46      |
+| 4         | 4.89      | 5.74      | 4.05      |
+| 4.5       | 5.03      | 6.04      | 4.03      |
+| 5         | 4.94      | 5.76      | 4.11      |
 
 
 # Training
 
-Trained with full-paramaters fine-tuning on 8x RTX A6000-48GB (Ampere) for 5 epochs for 62 hours (12.5h/epoch) at a commodity cost of $390 ($80/epoch).
+Trained with full-parameters fine-tuning on 8x RTX A6000-48GB (Ampere) for 5 epochs for 62 hours (12.5h/epoch) at a commodity cost of $390 ($80/epoch).
 We did not use [MultiPack](https://github.com/imoneoi/multipack_sampler) packing, as training was begun prior to implementing support for it in Axolotl for this new model type.
 
 [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)