update readme

README.md

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 We are thrilled to announce the alpha release of Flux.1 Lite, an 8B parameter transformer model distilled from the FLUX.1-dev model.
 
-Our goal? To distill FLUX.1-dev into a lighter model, reducing the parameters to just 24 GB, so it can run smoothly on most consumer-grade GPU cards, making high-quality AI models accessible to everyone.
+Our goal? To distill FLUX.1-dev further until we achieve to reduce the parameters to just 24 GB, so it can run smoothly on most consumer-grade GPU cards, making high-quality AI models accessible to everyone.
 
 ![Flux.1 Lite vs FLUX.1-dev](./sample_images/models_comparison.png)
 
@@ -25,7 +25,7 @@ Our goal? To distill FLUX.1-dev into a lighter model, reducing the parameters to
 
 As stated by other members of the community like [Ostris](https://ostris.com/2024/09/07/skipping-flux-1-dev-blocks/), it seems that blocks of the Flux1.dev transformer have a different contribution to the final image generation. To explore this, we analyzed the Mean Squared Error (MSE) between the input and output of each block, revealing significant variability. 
 
-Our findings? Not all blocks are created equal. By strategically skipping less impactful blocks, we've managed to achieve incredible efficiency gains without compromising on quality. The results are striking: skipping just one of the early MMDIT blocks can significantly impact model performance, whereas other blocks have a much smaller effect.
+Our findings? Not all blocks contribute equally. The results are striking: skipping just one of the early MMDIT blocks can significantly impact model performance, whereas skipping the rest of the blocks do not have a significant impact over the final image quality.
 
 ![Flux.1 Lite generated image](./sample_images/skip_blocks/generated_img.png)
 ![MSE MMDIT](./sample_images/skip_blocks/mse_mmdit_img.png)