update README

README.md

@@ -105,7 +105,7 @@ To achieve optimal performance, we recommend the following settings:
 1. **Sampling Parameters**:
    - For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, `MinP=0`, and `PresencePenalty=1.5`. **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions.
    - For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, `MinP=0`, and `PresencePenalty=1.5`.
-   - We recommend setting `presence_penalty` to 1.5 for quantized models to suppress repetitive outputs. You can adjust the `presence_penalty` parameter between 0 and 2. A higher value may occasionally lead to language mixing and a slight reduction in model performance. 
+   - **We recommend setting `presence_penalty` to 1.5 for quantized models to suppress repetitive outputs.** You can adjust the `presence_penalty` parameter between 0 and 2. A higher value may occasionally lead to language mixing and a slight reduction in model performance. 
 
 2. **Adequate Output Length**: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.