Update README.md

README.md

@@ -34,11 +34,14 @@ With its robust natural language processing capabilities, **Codepy 3B Deep Think
 | `tokenizer.json`                  | 17.2 MB        | Full tokenizer vocabulary and merges.           | Uploaded (LFS)    |
 | `tokenizer_config.json`           | 57.5 kB        | Tokenizer configuration details.                | Uploaded          |
 
-| Model Type | Size | Context Length | Link |
-|------------|------|----------------|------|
-| GGUF | 3B | - | [🤗 Codepy-Deepthink-3B-GGUF](https://huggingface.co/prithivMLmods/Codepy-Deepthink-3B-GGUF) |
+# **Run with LM Studio**
+
+| Feature Run                  | Details                                                                                       |
+|--------------------------|-----------------------------------------------------------------------------------------------|
+| **Run with LM Studio**   | https://lmstudio.ai/                                                         |
+| **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
@@ -143,14 +146,6 @@ To customize the password length, modify the `password_length` variable in the s
 - 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., `!@#$%^&*`).
 
----
-# **Run with LM Studio**
-
-| Feature Run                  | Details                                                                                       |
-|--------------------------|-----------------------------------------------------------------------------------------------|
-| **Run with LM Studio**   | https://lmstudio.ai/                                                         |
-| **Demo on LM Studio**    | https://drive.google.com/file/d/1CHdfjYrwMnk9ACvS40Abfy3xNXnCubKG/view?usp=sharing |
-
 # **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.