Update README.md

README.md

@@ -119,7 +119,7 @@ F2LLM-v2 is fully open. We release base models in 5 sizes, instruct models in 8
 
 The F2LLM-v2 family set a new state-of-the-art on a wide range of MTEB benchmarks, including Code, European, Scandinavian, German, French, Spanish, Polish, Dutch, Japanese, Vietnamese, Thai, Indic, Persian, among others.
 
-<img src="img/performance.png" width="60%" alt="Performance">
+<img src="img/performance.png" width="100%" alt="Performance">
 
 For details, refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
 
@@ -211,7 +211,7 @@ For symmetric tasks such as STS, clustering, and bitext mining, you can encode t
 
 This model is trained with Matryoshka Representation Learning (MRL), allowing for a superior tradeoff between performance and embedding size. You can truncate the embeddings to keep only the first `d` dimensions to reduce storage and speed up vector search in downstream systems. The model is trained with a smallest Matryoshka dimension of 8.
 
-<img src="img/mrl.png" width="40%" alt="MRL results">
+<img src="img/mrl.png" width="80%" alt="MRL results">
 
 Example:
 ```python