size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif
---
size_categories: n<1K
dataset_info:
  features:
  - name: prompt
    dtype: string
  - name: generation
    dtype: string
  splits:
  - name: train
    num_bytes: 7691
    num_examples: 12
  download_size: 7788
  dataset_size: 7691
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
---

<p align="left">
  <a href="https://github.com/argilla-io/distilabel">
    <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
  </a>
</p>

# Dataset Card for tinyllamamodel

This dataset has been created with [distilabel](https://distilabel.argilla.io/).



## Dataset Summary

This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:

```console
distilabel pipeline run --config "https://huggingface.co/datasets/aycankatitas/tinyllamamodel/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/aycankatitas/tinyllamamodel/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


<details><summary> Configuration: default </summary><hr>

```json
{
    "generation": " Overall, our goal is to provide insights that help organizations better understand and leverage the potential benefits of incorporating technology into their knowledge management strategies.",
    "prompt": "In this research, we aim to investigate how technology can moderate the correlation between knowledge management practices and the overall performance of an organization. This analysis will focus on specific technological tools and their impact on knowledge management in relation to various aspects of organizational performance. Additionally, we will explore how this relationship may vary across different industries and organizational structures."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("aycankatitas/tinyllamamodel", "default")
```

Or simply as it follows, since there's only one configuration and is named `default`: 

```python
from datasets import load_dataset

ds = load_dataset("aycankatitas/tinyllamamodel")
```


</details>




