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: instruction
    dtype: string
  - name: distilabel_metadata
    struct:
    - name: raw_input_text_generation_1
      list:
      - name: content
        dtype: string
      - name: role
        dtype: string
    - name: raw_output_text_generation_1
      dtype: string
  - name: model_name
    dtype: string
  - name: response
    dtype: string
  splits:
  - name: train
    num_bytes: 1121
    num_examples: 1
  download_size: 11096
  dataset_size: 1121
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 huggingface-smol-course-instruction-tuning-dataset

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/darkmater/huggingface-smol-course-instruction-tuning-dataset/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/darkmater/huggingface-smol-course-instruction-tuning-dataset/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "distilabel_metadata": {
        "raw_input_text_generation_1": [
            {
                "content": "What is the main focus of the Hugging Face Smol-Course?",
                "role": "user"
            }
        ],
        "raw_output_text_generation_1": "The Hugging Face Smol-Course is an online learning platform that focuses on deep learning and neural networks. It provides comprehensive courses for beginners to advanced learners in these fields. The course covers topics such as supervised and unsupervised learning, neural network architectures like convolutional and recurrent neural networks (CNN and RNN), and more. It also includes practical exercises and projects to help students apply their knowledge practically."
    },
    "instruction": "What is the main focus of the Hugging Face Smol-Course?",
    "model_name": "HuggingFaceTB/SmolLM2-1.7B-Instruct",
    "response": "The Hugging Face Smol-Course is an online learning platform that focuses on deep learning and neural networks. It provides comprehensive courses for beginners to advanced learners in these fields. The course covers topics such as supervised and unsupervised learning, neural network architectures like convolutional and recurrent neural networks (CNN and RNN), and more. It also includes practical exercises and projects to help students apply their knowledge practically."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("darkmater/huggingface-smol-course-instruction-tuning-dataset", "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("darkmater/huggingface-smol-course-instruction-tuning-dataset")
```


</details>




