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# Dataset: Stratified Instruction Fine-Tuning with Function Calling, Chat Data, and Reasoning Examples

## Overview

This dataset is a curated collection of 80,000 examples designed for instruction fine-tuning with a focus on function calling, chat-based interactions, and reasoning tasks. It is a combination of data from two primary sources: a stratified subset of the **mlabonne/FineTome-100k** dataset and a substantial sample from the **lilacai/glaive-function-calling-v2-sharegpt** dataset. This dataset has been carefully constructed to provide a balanced and diverse set of examples that are valuable for training advanced conversational models with function-calling capabilities.

## Dataset Composition

### 1. **Stratified Subset of mlabonne/FineTome-100k**
   - **Source**: [mlabonne/FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k)
   - **Description**: A stratified sample of 50,000 examples was drawn from this dataset, ensuring that the distribution of sources in this subset closely mirrors that of the original dataset. The sources include a variety of instruction-following data, chat interactions, and reasoning tasks, providing a robust base for fine-tuning models aimed at conversational understanding and reasoning.
   
### 2. **Sample from lilacai/glaive-function-calling-v2-sharegpt**
   - **Source**: [lilacai/glaive-function-calling-v2-sharegpt](https://huggingface.co/datasets/lilacai/glaive-function-calling-v2-sharegpt)
   - **Description**: An additional 30,000 examples were selected from this dataset, specifically focusing on instances that involve function calls within chat-based interactions. This component enriches the overall dataset by introducing structured, task-oriented dialogues that are critical for training models capable of handling function calls during conversations.

## Total Dataset Size

- **Total Examples**: 80,000
  - **50,000 examples** from the stratified subset of mlabonne/FineTome-100k
  - **30,000 examples** from lilacai/glaive-function-calling-v2-sharegpt

## Dataset Features

- **Function Calls**: A significant portion of the dataset includes examples where function calls are made within the context of a conversation. These examples are particularly useful for training models that need to handle dynamic and context-aware tasks during interactions.
- **Chat Data**: The dataset includes a variety of chat-based interactions, ranging from simple question-answer pairs to more complex dialogues. This diversity in conversation styles helps in fine-tuning models for natural language understanding and generation.
- **Reasoning Examples**: Reasoning tasks are well-represented in the dataset, providing a rich source of examples where models must infer, deduce, or reason through given information. This is essential for improving the model's cognitive abilities during interaction.

## Usage

This dataset is ideal for training and fine-tuning conversational AI models, especially those designed to handle structured data or tasks within a conversation, such as function calling or reasoning-based interactions. The balanced mix of data types ensures that the resulting models can perform well across a wide range of conversational scenarios.

## Citation

If you use this dataset in your research or work, please cite the original datasets:

- [mlabonne/FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k)
- [lilacai/glaive-function-calling-v2-sharegpt](https://huggingface.co/datasets/lilacai/glaive-function-calling-v2-sharegpt)