Update README.md

README.md

@@ -143,6 +143,49 @@ Quote for Motivation:
 
 # "To grow as a professional, set goals just beyond your current abilities. Achieving these milestones will not only overcome obstacles but also strengthen your skillset. If your tasks are too easy, you’ll never challenge yourself or improve, and life will pass you by!"
 
+### General Intenal Methods:
+
+Trained for multi-task operations as well as rag and function calling :
+
+This model is a fully functioning model and is fully uncensored: 
+
+the model has been trained on multiple datasets on the huggingface hub and kaggle :
+
+the focus has been mainly on methodology : 
+
+* Chain of thoughts
+* step by step planning
+* tree of thoughts
+* forest of thoughts
+* graph of thoughts
+* agent generation : Voting, ranking, ... dual agent response generation:
+
+
+## Training Reginmes:
+  * Alpaca
+  * ChatML / OpenAI / MistralAI
+  * Text Generation
+  * Question/Answer (Chat)
+  * Planner
+  * Instruction/Input/Response (instruct)
+  * Mistral Standard Prompt
+  * Translation Tasks
+  * Entitys / Topic detection
+  * Book recall
+  * Coding challenges, Code Feedback, Code Sumarization, Commenting Code, code planning and explanation: Software generation tasks
+  * Agent Ranking and response anyalisis
+  * Medical tasks
+    * PubMed
+    * Diagnosis
+    * Psychaitry
+    * Counselling
+    * Life Coaching
+    * Note taking
+    * Medical smiles
+    * Medical Reporting
+  * Virtual laboritys simulations
+  * Chain of thoughts methods
+  * One shot / Multi shot prompting tasks
 
 
 ## THE REFINED CHAT MODEL :
@@ -260,66 +303,3 @@ Final Thought: Summarize your reasoning and provide a clear answer to the questi
 
 
 
-## Training Reginmes:
-  * Alpaca
-  * ChatML / OpenAI / MistralAI
-  * Text Generation
-  * Question/Answer (Chat)
-  * Planner
-  * Instruction/Input/Response (instruct)
-  * Mistral Standard Prompt
-  * Translation Tasks
-  * Entitys / Topic detection
-  * Book recall
-  * Coding challenges, Code Feedback, Code Sumarization, Commenting Code, code planning and explanation: Software generation tasks
-  * Agent Ranking and response anyalisis
-  * Medical tasks
-    * PubMed
-    * Diagnosis
-    * Psychaitry
-    * Counselling
-    * Life Coaching
-    * Note taking
-    * Medical smiles
-    * Medical Reporting
-  * Virtual laboritys simulations
-  * Chain of thoughts methods
-  * One shot / Multi shot prompting tasks
-
-### General Intenal Methods:
-
-Trained for multi-task operations as well as rag and function calling :
-
-This model is a fully functioning model and is fully uncensored: 
-
-the model has been trained on multiple datasets on the huggingface hub and kaggle :
-
-the focus has been mainly on methodology : 
-
-* Chain of thoughts
-* step by step planning
-* tree of thoughts
-* forest of thoughts
-* graph of thoughts
-* agent generation : Voting, ranking, ... dual agent response generation:
-
-
-# Training Philosophy
-
-Here are some of the benefits you might experience by prioritizing attention mechanisms during fine-tuning:
-
-## Enhanced Contextual Understanding: 
-
-Fine-tuning attention layers helps the model better grasp the relationships and dependencies within the input data, leading to more contextually relevant and accurate outputs.
-## Improved Control over Generation: 
-
-You gain more control over the model's generation process, guiding it to focus on specific aspects of the input and produce outputs that align with your desired goals.
-## More Creative and Diverse Outputs: 
-
-By refining the attention mechanism, you can encourage the model to explore a wider range of possibilities and generate more creative and diverse responses.
-## Reduced Overfitting: 
-
-Fine-tuning with a focus on attention can help prevent overfitting to specific patterns in the training data, leading to better generalization and more robust performance on new inputs.
-
-# “Epochs are the key to effective training, rather than merely mass dumping examples—unless those examples are interconnected within a single or multiple conversations that teach through dialogue.”
-