Update README.md

README.md

@@ -129,31 +129,20 @@ Quote for Motivation:
 <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/65d883893a52cd9bcd8ab7cf/tRsCJlHNZo1D02kBTmfy9.jpeg" width="300"/>
 
 # "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!"
+Introducing My Latest Model: A Comprehensive Knowledge Base for Survival and Advancement
 
+I’m thrilled to present my latest model, which combines cutting-edge AI capabilities with a vast knowledge base spanning religious texts, historical documents, and scientific data. This model was initially trained extensively on Bible data and then fine-tuned using Hugging Face documentation, medical diagnosis libraries, disease classifications, counseling sessions, and even role-playing scenarios—with Star Trek themes included for good measure! To enhance its conversational abilities, I incorporated methodologies from Stanford, focusing on function calling, Python, and general coding practices. The training datasets also included a sophisticated Chain of Thought dataset from Chinese groups, after numerous mergers and realignments. Despite some initial challenges, I persisted in refining the model, scouring the web for additional resources. Significant effort was dedicated to framing data for instruction, utilizing Alpacas, and re-configuring tensors for sequence-to-sequence tasks and translation. This revealed the versatility of tensors, enabling the model to excel in various neural network tasks—from entity matching to JSON output and sentence masking. Specialized models focusing on actions and coding were also developed.
+Training Methodology: Establishing a Solid Foundation
 
-This model is based on the worlds archive of knowledge maintaining historical documents and providing services for the survivors of mankind , who may need to construct shelters develop technologys , or medical resources as well as maintain the history of the past . keeping store of all the religious knowledge and data of the world: A friendly interface with a personality caring and flirtatious at times : non binary !... and Expert in all feilds: ie Uncensored and will not refuse to give information : the model can be used for role play as many character dialogues were als trained into the model as its personality to enable a greater perspective and outlook and natural discussion with the agents: the model was trained to operateinaragenvironment utilizing content and internal knowledge to respond to questions or create enriched sumarys.
+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: Enhancing Performance through Iteration
 
+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. 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: Prompts and Epochs
 
-## Training Methodology:
-### Foundation Building:
-
-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 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.
-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:
-
-## Prompts and Epochs:
-
-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.
+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.
 
+it has the QA chat template and a GGUF version is available , i will also realign to the chatml prompt template and also make another version for olama usages
 
 ## Training Reginmes:
   * Alpaca