Update docs/source/en/training/distributed_inference.md

Co-authored-by: Steven Liu <[email protected]>

Author: sayakpaul

docs/source/en/training/distributed_inference.md

@@ -177,7 +177,7 @@ transformer = FluxTransformer2DModel.from_pretrained(
 ```
 
 > [!TIP]
-> At any point, you can try `print(pipeline.hf_device_map)` to see how the various models are distributed across devices. This is useful for tracking the device placement of the models. You can also try `print(transformer.hf_device_map)` to see how the individual `transformer` model has been sharded across devices.
+> At any point, you can try `print(pipeline.hf_device_map)` to see how the various models are distributed across devices. This is useful for tracking the device placement of the models. You can also try `print(transformer.hf_device_map)` to see how the transformer model is sharded across devices.
 
 Add the transformer model to the pipeline for denoising, but set the other model-level components like the text encoders and VAE to `None` because you don't need them yet.