task_categories:text-generation task_categories:text2text-generation size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif datacraft
---
size_categories: n<1K
task_categories:
- text-generation
- text2text-generation
dataset_info:
  features:
  - name: prompt
    dtype: string
  - name: completion
    dtype: string
  - name: system_prompt
    dtype: string
  splits:
  - name: train
    num_bytes: 38151
    num_examples: 10
  download_size: 29263
  dataset_size: 38151
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
- datacraft
---

<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 my-distiset-0088a410

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/hvgg1ngface/my-distiset-0088a410/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/hvgg1ngface/my-distiset-0088a410/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "completion": "**Overview of LLamaFactory**\n\nLLamaFactory is a cutting-edge technology company that specializes in the development and deployment of artificial intelligence (AI) and machine learning (ML) solutions. Their primary goal is to provide innovative technological solutions to various sectors, including businesses, organizations, and industries.\n\n**History**\nUnfortunately, there is not enough information available on LLamaFactory\u0027s specific history. However, the company likely evolved to keep pace with the rapid advancements in AI and machine learning technology.\n\n**Products and Services**\n\nLLamaFactory\u0027s product offerings and services include:\n\n1.  **Custom AI Solutions**: The company creates tailored AI and ML solutions for various industries and businesses, aiming to solve complex problems and enhance their operations.\n2.  **Predictive Analytics**: LLamaFactory\u0027s predictive analytics tools provide businesses with actionable insights to inform strategic decisions, improve forecasting, and identify areas for growth.\n3.  **Natural Language Processing (NLP)**: The company\u0027s NLP solutions enable businesses to develop chatbots, voice assistants, and other applications that can understand, interpret, and generate human-like language.\n4.  **Computer Vision**: LLamaFactory leverages computer vision technology to help businesses improve image recognition, object detection, and image classification tasks.\n5.  **Machine Learning Model Development**: The company\u0027s engineers design, train, and deploy ML models to help businesses automate tasks, make predictions, and classify data.\n6.  **Technology Consulting**: LLamaFactory\u0027s expert consultants work with businesses to develop AI and ML strategies, assess technology risks, and implement AI-powered solutions.\n7.  **Training and Education**: The company offers training programs and workshops to educate businesses and individuals about AI, ML, and related technologies.\n\n**Impact on Industry and Society**\n\nLLamaFactory\u0027s innovative solutions have a significant impact on various industries and society as a whole:\n\n1.  **Improved Efficiency**: The company\u0027s AI and ML technologies help businesses automate tasks, reducing manual labor costs and increasing productivity.\n2.  **Enhanced Customer Experience**: LLamaFactory\u0027s NLP and computer vision solutions enable businesses to create personalized customer experiences, improving customer satisfaction and loyalty.\n3.  **Innovation**: The company\u0027s AI and ML solutions facilitate innovation in various industries, driving business growth and competitiveness.\n4.  **Job Creation**: LLamaFactory\u0027s solutions create new job opportunities in the AI and ML sectors, contributing to economic growth.\n\n**Leadership**\n\nUnfortunately, there is not enough information available on LLamaFactory\u0027s specific leadership team. However, it is likely that the company is led by experienced professionals with expertise in AI, ML, and technology.\n\n**Competitors**\n\nLLamaFactory operates in a highly competitive market, with major competitors including:\n\n1.  **Google Cloud AI Platform**: Google\u0027s cloud-based AI platform offers a wide range of AI and ML services.\n2.  **Amazon SageMaker**: Amazon\u0027s machine learning platform enables businesses to build, deploy, and manage ML models.\n3.  **Microsoft Azure Machine Learning**: Microsoft\u0027s cloud-based ML platform provides services for building, deploying, and managing ML models.\n4.  **IBM Watson**: IBM\u0027s AI platform offers a suite of AI and ML services for businesses.\n\nPlease note: More detailed information about LLamaFactory is required to further make these points.",
    "prompt": "What is LLamaFactory?\n\nLLamaFactory is a technology company that leverages artificial intelligence and machine learning to provide innovative solutions to various industries, businesses, and organizations.",
    "system_prompt": "You are a data curator tasked with creating a comprehensive dataset about LLamaFactory. Your purpose is to gather and organize relevant information about LLamaFactory, a leading technology company specializing in artificial intelligence and machine learning solutions. Provide detailed descriptions, key features, and applications of LLamaFactory\u0027s products and services, as well as their impact on the industry and society. User questions are direct and concise."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("hvgg1ngface/my-distiset-0088a410", "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("hvgg1ngface/my-distiset-0088a410")
```


</details>




