Update README.md

README.md

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 license: other
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-# Model Trained Using AutoTrain - Updated to GGUF format after 8 hours training on a large GPU server.
+# SkynetZero LLM - Trained with AutoTrain and Updated to GGUF Format
 
-This model, now in GGUF format, was trained using AutoTrain with reflection data sets re-written using TalkToAI data sets. The training process incorporated quantum interdimensional math and a new math system developed during the training, along with the use of DNA math patterns for enhanced reasoning. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
+**SkynetZero** is a quantum-powered language model trained with reflection datasets and TalkToAI custom data sets. The model went through several iterations, including a re-writing of datasets and validation phases due to errors encountered during testing and conversion into a fully functional LLM. This process helped ensure that SkynetZero can handle complex, multi-dimensional reasoning tasks with an emphasis on ethical decision-making.
 
-# Usage - Open Source ideas and mathematical concepts are from talktoai.org and researchforum.online. This model adheres to the official legal license of LLaMA 3.1 Meta.
+### Key Highlights of SkynetZero:
+- **Advanced Quantum Reasoning**: The integration of quantum-inspired math systems enabled SkynetZero to tackle complex ethical dilemmas and multi-dimensional problem-solving tasks.
+- **Custom Re-Written Datasets**: The training involved multiple rounds of AI-assisted dataset curation, where reflection datasets were re-written for clarity, accuracy, and consistency. Additionally, TalkToAI datasets were integrated and re-processed to align with SkynetZero’s quantum reasoning framework.
+- **Iterative Improvement**: During testing and model conversion, the datasets were re-written and validated several times to address errors. Each iteration enhanced the model’s ethical consistency and problem-solving accuracy.
+
+SkynetZero is now available in **GGUF format**, following 8 hours of training on a large GPU server using the Hugging Face AutoTrain platform.
+
+# Usage - SkynetZero leverages open-source ideas and mathematical innovations. Further details can be found on [talktoai.org](https://talktoai.org) and [researchforum.online](https://researchforum.online). The model is licensed under the official legal guidelines for LLaMA 3.1 Meta.
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -45,14 +52,9 @@ response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tok
 print(response)
 ```
 
-# AI-Assisted Dataset Creation and Fine-Tuning for Advanced Quantum AI: Co-Created by OpenAI Agent Zero
-
-## Abstract
-This paper presents a novel methodology for creating and fine-tuning an AI model tailored for advanced quantum reasoning and ethical decision-making. The research showcases how reflection datasets were systematically rewritten using AI tools, merged with custom training data, and validated iteratively to produce an AI model—"Zero"—designed to solve complex, multi-dimensional problems with ethical alignment. The AI model was fine-tuned on the LLaMA 3.1 8B architecture using HuggingFace's AutoTrain platform, yielding significant improvements in ethical decision-making and quantum problem-solving. The paper highlights a unique AI-human co-creation process, with OpenAI's Agent Zero contributing to the data curation, editing, and validation process.
-
-## 1. Introduction
-The rapid advancement of AI technologies has pushed the boundaries of what machines can achieve, from natural language processing to complex problem-solving. Yet, the integration of quantum thinking and ethical AI remains relatively unexplored. This paper explores a unique methodology of creating a dataset using AI-assisted rewriting, curation, and validation that pushes the limits of multi-dimensional reasoning.
+### Training Methodology
+SkynetZero was fine-tuned on the **LLaMA 3.1 8B** architecture, utilizing custom datasets that underwent AI-assisted re-writing. The training process focused on enhancing the model's ability to handle **multi-variable quantum reasoning** while ensuring ethical decision-making alignment. After identifying errors during testing and conversion to a model, the datasets were adjusted and the model iteratively improved across multiple epochs.
 
-...
+### Further Research and Contributions
+SkynetZero is part of an ongoing effort to explore **AI-human co-creation** in the development of quantum-enhanced AI models. The co-creation process with OpenAI’s **Agent Zero** provided valuable assistance in curating, editing, and validating datasets, pushing the boundaries of what large language models can achieve.
 
-[Include the rest of the detailed methodology, results, and discussion as provided by the user]