Update README.md

README.md

@@ -94,7 +94,7 @@ The dataset consists of multiple reasoning distillation sources:
 | Applied / Social Systems (Urban Planning, Traffic, Supply Chain, etc.) | 360 | 2.6% |
 | Other | 264 | 1.9% |
 
-⚠️ **Distillation & Task-Specific Fine-Tuning Effects:** This model has been further distilled and fine-tuned on top of the base model for reasoning-oriented tasks. While these techniques may improve performance on certain specialized tasks, they may also affect the model’s generalization ability in broader scenarios and can potentially lead to partial forgetting of some pretraining knowledge. The extent of these effects depends in part on the quality, scale, and distribution of the training datasets used during distillation and fine-tuning. As a result, the model’s behavior may differ from the base model across different tasks or application contexts. Users are encouraged to evaluate the model according to their specific requirements before deployment.
+⚠️ **Distillation & Task-Specific Fine-Tuning Effects:** This model has been distilled and further fine-tuned on top of the base model for reasoning-oriented tasks. These techniques may improve performance on certain specialized tasks, but they can also influence the model’s generalization ability in broader scenarios and may lead to partial forgetting of some pretraining knowledge. The extent of these effects depends on factors such as the quality, scale, and distribution of the datasets used during distillation and fine-tuning. As a result, the model’s behavior may differ from the base model across different tasks or application contexts. Users are encouraged to evaluate the model according to their specific requirements before deployment. Thank you for your understanding～
 
 
 
@@ -112,4 +112,4 @@ The dataset consists of multiple reasoning distillation sources:
 - **Preview Version Notice:** As a relatively specialized distilled reasoning model, surrounding inference templates, prompt formatting strategies, and ecosystem integrations may still require tuning. Users may encounter occasional compatibility differences depending on runtime or deployment stack.
 
 ## 🙏 Acknowledgements
-Special thanks to the **Qwen** team for the strong base architecture, and to the broader open-source ecosystem for enabling efficient reasoning distillation workflows. We also acknowledge the value of the distilled reasoning corpora derived from **Gemini 3.1 Pro**, **Qwen3.5-27B**, and **Gemini 3 Pro**, which made this model possible.
+Special thanks to the **Qwen** team for the strong base architecture, and to the broader open-source ecosystem for enabling efficient reasoning distillation workflows. We also acknowledge the value of the distilled reasoning corpora derived from **Gemini 3.1 Pro**, **Qwen3.5**, and **Gemini 3 Pro**, which made this model possible.