Update README.md

README.md

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 ## 📌 Model Overview
 
-**TriCoAlign-1.5B** is a specialized Large Language Model fine-tuned from **Qwen2.5-1.5B** for **Network Intrusion Detection (NIDS)**. It implements the **TriCoAlign** framework proposed in the paper *"TriCoAlign: Cyclic Alignment for Stabilizing Large Language Models in Network Intrusion Detection"*.
+**TriCoAlign-0.5B** is a specialized Large Language Model fine-tuned from **Qwen2.5-0.5B** for **Network Intrusion Detection (NIDS)**. It implements the **TriCoAlign** framework proposed.
 
 Standard LLMs often suffer from **unstable reasoning behaviors** and **inconsistent decision outcomes** when analyzing network traffic (e.g., producing different labels for the same packet upon repeated inference). TriCoAlign addresses this by jointly aligning three complementary aspects in a cyclic manner:
 1.  **Format Alignment**: Enforces a structured `Question–Reasoning–Answer` output to decouple semantic roles.
@@ -53,7 +53,7 @@ This model transforms powerful but unstable LLM reasoning into reliable, deploya
 Evaluated on standard NIDS benchmarks, TriCoAlign demonstrates superior accuracy and stability compared to baselines.
 
 
-*> Note: Baseline numbers reflect the instability of raw LLMs as reported in the TriCoAlign paper. Our 1.5B variant maintains comparable robustness with lower computational cost.*
+*> Note: Baseline numbers reflect the instability of raw LLMs as reported in the TriCoAlign paper. Our 0.5B variant maintains comparable robustness with lower computational cost.*
 
 ## 💻 How to Use