Update README.md

README.md

@@ -232,7 +232,7 @@ Given these limitations observed in both models regarding token economy and cont
 - **Qwen2-1.5b:**
    The decision to utilize Qwen2 stemmed from its superior performance metrics where it achieved an impressive highest quality rating close to ~95%. Notably, this larger model supports up to a substantial 32k tokens in context length allowing comprehensive analysis over extended texts which is vital for educational content evaluation spanning multiple academic levels from elementary through university.
    
-   Additionally, despite being a larger scale model potentially implying higher computational demands; various optimized inference solutions such as Token Grouping Inference (TGI) or very large language models (vLLM) adaptations have been integrated effectively enhancing operational efficiency making real-time applications feasible without compromising on analytical depth or accuracy.
+   Additionally, despite being a larger scale model potentially implying higher computational demands; various optimized inference solutions such as TGI or vLLM adaptations have been integrated effectively enhancing operational efficiency making real-time applications feasible without compromising on analytical depth or accuracy.
    
 In conclusion, while earlier iterations with other models provided valuable insights into necessary features and performance thresholds; transitioning towards using Qwen2 has significantly advanced our project’s capability delivering refined assessments aligned closely with set objectives ensuring robustness scalability future expansions within this domain.