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README.md

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+# QuetzaCOaTl: Fine-tuned Multi-Turn Chain-of-Thought Reasoning Model
+
+## Model Description
+
+QuetzaCOaTl is a fine-tuned version of the Qwen2.5 - 7B-Instruct model, specialized in multi-turn chain-of-thought reasoning. This model excels at handling complex, multi-turn dialogues involving logical reasoning, mathematical problem-solving, and step-by-step analytical thinking.
+
+### Key Features
+
+1. **Enhanced Reasoning Capabilities:** Trained on structured conversations that promote step-by-step logical thinking and problem-solving.
+2. **Versatile Dialogue Handling:** Capable of engaging in short, medium, and long conversations with consistent quality and coherence.
+3. **Mathematical and Logical Prowess:** Skilled at tackling abstract logic puzzles and mathematical scenarios.
+4. **Structured Output:** Provides responses with clear, organized thought processes, often broken down into logical steps.
+5. **Multi-Turn Proficiency:** Excels in maintaining context and building upon previous turns in a conversation.
+
+## Use Cases
+
+- Academic research requiring complex reasoning
+- Educational tools for teaching critical thinking and problem-solving
+- Assisting in data analysis and interpretation
+- Enhancing decision-making processes in various fields
+- Supporting scientific hypothesis generation and testing
+- Improving AI-assisted coding and debugging
+
+## Model Specifications
+
+- **Base Model:** Qwen2.5 - 7B-Instruct
+- **Training Data:** Multi-Turn Chain-of-Thought Reasoning Dataset
+- **Input Format:** Follows the conversation structure of the training data, with clear delineation between user and assistant roles
+
+## Ethical Considerations
+
+While this model is designed for enhanced reasoning capabilities, users should be aware that:
+
+1. The model's outputs are based on its training data and should not be considered infallible. Critical evaluation of its responses is crucial, especially for important decisions.
+2. The model may exhibit biases present in its training data. Users should be vigilant and cross-verify information when necessary.
+3. The model's capabilities should not be used to generate or promote misinformation or harmful content.
+
+## Ollama
+
+A modelfile is included for easy importation into Ollama
+
+## Limitations
+
+- While the model excels at structured reasoning, it may struggle with tasks that require real-world knowledge beyond its training data.
+- The model's knowledge is limited to its training data cutoff and may not reflect the most current information.
+- As with all language models, outputs should be critically evaluated and fact-checked when used for sensitive or important applications.
+
+## Acknowledgements
+
+This model was fine-tuned using a specialized Multi-Turn Chain-of-Thought Reasoning Dataset. We acknowledge the creators and contributors of this dataset for enabling the development of advanced reasoning capabilities in language models.
+