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@@ -40,6 +40,7 @@ With its robust natural language processing capabilities, **Codepy 3B Deep Think
 | *Demo on LM Studio*    | https://drive.google.com/file/d/1CHdfjYrwMnk9ACvS40Abfy3xNXnCubKG/view?usp=sharing |
 | *Codepy-Deepthink-3B-GGUF* | https://huggingface.co/prithivMLmods/Codepy-Deepthink-3B-GGUF |
 # **<span style="color:red;">Sample Deepthink Inference</span>**
 >>> Develop a Python program to generate random passwords that consist of 8 characters. Not applicable
@@ -143,7 +144,7 @@ To customize the password length, modify the `password_length` variable in the s
 ## Security Notes
 - This implementation uses Python's `random` module, which is suitable for general-purpose randomness. For cryptographically secure passwords, consider using the `secrets` module.
 - The character set includes spaces for additional complexity, but you can modify the `characters` string to include other symbols (e.g., `!@#$%^&*`).
 # **Model Architecture**
  Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

 | *Demo on LM Studio*    | https://drive.google.com/file/d/1CHdfjYrwMnk9ACvS40Abfy3xNXnCubKG/view?usp=sharing |
 | *Codepy-Deepthink-3B-GGUF* | https://huggingface.co/prithivMLmods/Codepy-Deepthink-3B-GGUF |
+---
 # **<span style="color:red;">Sample Deepthink Inference</span>**
 >>> Develop a Python program to generate random passwords that consist of 8 characters. Not applicable
 ## Security Notes
 - This implementation uses Python's `random` module, which is suitable for general-purpose randomness. For cryptographically secure passwords, consider using the `secrets` module.
 - The character set includes spaces for additional complexity, but you can modify the `characters` string to include other symbols (e.g., `!@#$%^&*`).
+---
 # **Model Architecture**
  Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.