diff --git a/_posts/2024-09-25-pytorch-native-architecture-optimization.md b/_posts/2024-09-26-pytorch-native-architecture-optimization.md
similarity index 97%
rename from _posts/2024-09-25-pytorch-native-architecture-optimization.md
rename to _posts/2024-09-26-pytorch-native-architecture-optimization.md
index 1f219a49710d..fcf5122e970e 100644
--- a/_posts/2024-09-25-pytorch-native-architecture-optimization.md
+++ b/_posts/2024-09-26-pytorch-native-architecture-optimization.md
@@ -72,7 +72,7 @@ But also can do things like quantize weights to int4 and the kv cache to int8 to
 
 Post training quantization, especially at less than 4 bit can suffer from serious accuracy degradations. Using [Quantization Aware Training](https://pytorch.org/blog/quantization-aware-training/) (QAT) we’ve managed to recover up to 96% of the accuracy degradation on hellaswag. We’ve integrated this as an end to end recipe in torchtune with a minimal [tutorial](https://github.com/pytorch/ao/tree/main/torchao/quantization/prototype/qat)
 
-![](/assets/assets/Figure_3.png){:style="width:100%"}
+![](/assets/images/Figure_3.png){:style="width:100%"}
 
 # Training