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