---
datasets:
- Excido/Quetzacoatl
---
# QuetzaCOaTl: Fine-tuned Multi-Turn Chain-of-Thought Reasoning Model

![image/webp](https://cdn-uploads.huggingface.co/production/uploads/62ec82aaaefff8bcb1336b87/T3lvR0TOoSXFsAbMC-bk8.webp)

## Model Description

QuetzaCOaTl is a fine-tuned version of the Llama3.2-3B-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.

(EXPERIMENTAL)

EDIT: New 2000 step model.

### 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:** Llama3.2-3B-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.