Update README.md

README.md

@@ -15,35 +15,51 @@ Quote for Motivation:
 
 — Leroy Dyer (1972-Present)
 
-Model Overview:
+# Project Overview:
 
-The SpydazWeb AI React Project is built upon the SpydazWeb_AI_ChatQA_005/006 merged chat model as the foundation. The model was trained using a methodology inspired by the ReAct paper, which provides a framework for creating ReAct Agents capable of performing complex tasks. This approach equips the model with various methods of thought and action.
+The SpydazWeb AI React Project was initiated to build advanced AI agents capable of performing complex tasks using structured methods of thought and action. The project began with the SpydazWeb_AI_ChatQA_005/006 model as the base, which was subsequently trained using a methodology inspired by the ReAct paper. This training provided a solid foundation for developing ReAct Agents, designed to execute various tasks effectively.
 
-Training Process:
+## Training Methodology:
 
-Initial Training:
+### Foundation Building:
 
-The model was initially trained on binary yes/no questions without any methodology.
-The training began with a simple prompt (Prompt A) that introduced basic functionality, but with room for improvement.
-The model was later enhanced with a new and more flexible prompt, incorporating a handcrafted GPT-4.0 prompt to align with the personalized agent system. This improved the model’s adaptability to new methodologies and tasks.
-Prompt Design:
+The initial phase involved training the model on binary yes/no questions without any explicit methodology. This was crucial in establishing a baseline for the model’s decision-making capabilities.
+The model was first trained using a simple production prompt, known as Prompt A, which provided basic functionality. Although this prompt was imperfect, it fit the dataset and set the stage for further refinement.
+## Methodology Development:
 
-The model was exposed to different prompting strategies, including 1-shot and multi-shot prompting, to combat potential issues with large instruction sets.
-The focus was on providing the model with methods of extracting information rather than merely training it on the information itself.
-Methodology Training:
+The original prompt was later enhanced with a more flexible approach, combining elements from a handcrafted GPT-4.0 prompt. This adaptation aligned the model with my personal agent system, allowing it to better respond to diverse tasks and methodologies.
+I discovered that regularly updating the model with new methodologies significantly enhanced its performance. The iterative process involved refining prompts and experimenting with different training strategies to achieve optimal results.
+## Prompts and Epochs:
 
-The training emphasized teaching the model to plan and execute complex tasks, such as generating complete software without errors.
-By incorporating agency and workflow concepts, the model learned to collaborate effectively and improved its software development capabilities.
-Key Observations:
+I found that large prompts required multiple epochs to yield consistent results. However, fewer epochs were needed when prompts were simplified or omitted. The purpose of large prompts during training was to give the model a wide range of response styles, allowing it to adjust parameters for various tasks.
+This approach helped the model internalize methodologies for extracting information, which is central to fine-tuning. The training emphasized teaching the model to plan and execute complex tasks, such as generating complete software without errors.
+## Key Findings:
 
-Self-Correction: The model demonstrated an ability to self-correct by comparing its responses to expected outcomes. This self-check mechanism, especially in calculations, led to more accurate results.
-Internal Querying (Self-RAG): The model was trained to query itself before providing a final response, effectively creating a multi-step internal process for generating more thoughtful and accurate answers. This process is referred to as "self-RAG" (self-retrieval-augmented generation).
-Tool-Based Model: The model’s performance was enhanced by using tools for thinking and reflecting, though this made it slower on hardware like an RTX 2030.
-Future Goals:
+### Self-Correction and Thought Processes:
 
-Dataset Development: The goal is to develop a dataset where the model not only performs functions but also interacts with users to gather additional information for more refined responses.
-Training Focus: Training should prioritize the steps required to achieve a goal rather than the end target itself, ensuring that the model is capable of navigating complex tasks independently.
+During training, I observed that the model could self-correct by comparing its responses to expected outcomes, particularly in calculations. This self-check mechanism allowed the model to reflect on its answers and improve its accuracy.
+I introduced the concept of "self-RAG" (self-retrieval-augmented generation), where the model queries itself before providing a final response. This internal process allowed the model to generate more thoughtful and accurate answers by simulating a multi-step internal dialogue.
+Tool-Based Reasoning:
 
+A significant portion of the training focused on enabling the model to use tools effectively. For instance, if the model needed to think, it would use a "think tool" that queried itself and provided an internal response. This tool-based approach was instrumental in enhancing the model’s reasoning capabilities, though it slowed down the response time on certain hardware like the RTX 2030.
+Despite the slower response time, the model’s ability to perform complex internal queries resulted in more accurate and well-reasoned outputs.
+Training for Comprehensive Responses:
+
+One key finding was that the model initially struggled with generating complete software without errors. After training the model on planning and agency concepts, it showed significant improvement in developing complete projects. This highlighted the importance of training the model not just on individual tasks, but on the overall processes required to achieve a common goal.
+Challenges and Refinements:
+
+### Large Prompts vs. Simplified Training:
+I noticed that while large prompts during training can offer the model more selection in its responses, they can also reduce the effectiveness if not handled correctly. Over-prompting led to a need for multiple epochs, whereas simpler prompts required fewer epochs. This balance between prompt size and training depth was crucial in fine-tuning the model.
+The model's performance was evaluated across different prompting strategies, including 1-shot and multi-shot prompting, to determine the most effective approach for various tasks.
+Future Directions:
+
+### Dataset Expansion:
+
+I aim to develop a dataset where the model can not only perform specific functions but also interact with users to gather additional information. This will enable the model to refine its responses and provide more accurate and contextually relevant answers.
+The focus of future training will be on the process of achieving a goal, ensuring that the model can navigate complex tasks independently and effectively.
+Real-Time Feedback:
+
+In future iterations, I plan to incorporate a feature where the model informs the user of its internal processes, such as when it is thinking or performing actions. This real-time feedback will enhance communication between the user and the model, maintaining an effective conversational flow.
 
 ## Prompt A: 
 ```yaml