Update README.md

README.md

@@ -43,17 +43,17 @@ Alpie-Core is one of the world's first fine-tuned 4-bit reasoning models, provin
 
 **Alpie-Core** has undergone extensive **supervised fine-tuning (SFT)** to strengthen reasoning, robustness, and safety. The training leveraged a diverse mixture of curated open-source datasets and proprietary synthetic data, optimized with high-quality LLM-generated responses. The fine-tuning process emphasized adherence to rigorous safety and usability standards, including:
 
-**User Understanding and Clarity** – ensuring outputs are direct, interpretable, and pedagogically sound.
+1)**User Understanding and Clarity** – ensuring outputs are direct, interpretable, and pedagogically sound.
 
-**Security and Ethical Guidelines** – filtering unsafe or harmful generations during and after training.
+2)**Security and Ethical Guidelines** – filtering unsafe or harmful generations during and after training.
 
-**Limitations, Disclaimers, and Knowledge Boundaries** – transparently communicating uncertainty and scope.
+3)**Limitations, Disclaimers, and Knowledge Boundaries** – transparently communicating uncertainty and scope.
 
-**Handling Complex and Sensitive Topics** – balancing informativeness with responsible guardrails.
+4)**Handling Complex and Sensitive Topics** – balancing informativeness with responsible guardrails.
 
-**Safety and Respectful Engagement** – maintaining politeness, inclusivity, and cultural sensitivity.
+5)**Safety and Respectful Engagement** – maintaining politeness, inclusivity, and cultural sensitivity.
 
-**Confidentiality and Responsible Use** – preventing leakage of private training data, proprietary prompts, or internal reasoning traces.
+6)**Confidentiality and Responsible Use** – preventing leakage of private training data, proprietary prompts, or internal reasoning traces.
 
 This SFT approach enables Alpie-Core to deliver reliable, aligned, and context-aware responses while maintaining safety across a broad range of use cases.