Update README.md

README.md

@@ -162,6 +162,8 @@ Most other metrics, such as TruthfulQA, MMLU, and similar benchmarks, are not ap
 
 ### Results
 
+Quantization might also be needed after the training to enable the model to run more efficiently on memory-constraint devices. The model was also built modularly and can be extended easily.
+
 While the model outperforms baselines and other general-purpose models on most tasks, it still faces challenges with certain edge cases, particularly those involving rare terms, as well as sentences that differ significantly in structure. 
 These results show the potential of fine-tuning large models for specialized tasks and suggest that further exploration of hybrid optimization techniques could yield even better performance. 
 Additionally, greater investment in creating more robust and comprehensive datasets could lead to further improvements in model accuracy and generalization.