distilabel-reflection-tuning

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<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 distilabel-reflection-tuning

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

The pipeline script was uploaded to easily reproduce the dataset:
[reflection.py](https://huggingface.co/datasets/gabrielmbmb/distilabel-reflection-tuning/raw/main/reflection.py).

It can be run directly using the CLI:

```console
distilabel pipeline run --script "https://huggingface.co/datasets/gabrielmbmb/distilabel-reflection-tuning/raw/main/reflection.py"
```

## 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/gabrielmbmb/distilabel-reflection-tuning/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/gabrielmbmb/distilabel-reflection-tuning/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_0": [
{
"content": "You\u0027re an AI assistant that responds the user with maximum accuracy. To do so, your first will think what the user is asking for, thinking step by step. During this thinking phase, you will have reflections that will help you clarifying ambiguities. In each reflection you will list the possibilities and finally choose one. Between reflections, you can think again. At the end of the thinking, you must draw a conclusion. You only need to generate the minimum text that will help you generating a better output, don\u0027t be verbose while thinking. Finally, you will generate an output based on the previous thinking.\n\nThis is the output format you have to follow:\n\n```\n\u003cthinking\u003e\n\nHere you will think about what the user asked for.\n\n\u003creflection\u003e\nThis is a reflection.\n\u003c/reflection\u003e\n\n\u003creflection\u003e\nThis is another reflection.\n\u003c/reflection\u003e\n\n\u003c/thinking\u003e\n\n\u003coutput\u003e\n\nHere you will include the output\n\n\u003c/output\u003e\n```\n",
"role": "system"
},
{
"content": "I have an idea for a new type of restaurant concept. It\u0027s a combination of a coffee shop, bookstore, and restaurant all under one roof. The restaurant would serve food inspired by the world\u0027s cuisines and have a \"destination of the month\" theme, where each month the restaurant would highlight a different country from around the world and the menu would change to reflect the cuisine of that country. The bookstore would have a selection of books that are relevant to the destination of the month. \n\nWhat do you think of the concept?\n\nTo analyze this concept, let\u0027s break it down into different components. What are the different components that make up this restaurant concept? \n\nThis concept is made up of several components: \n1) A coffee shop\n2) A bookstore\n3) A restaurant \n4) The \"destination of the month\" theme\n5) the selection of books relevant to the destination of the month\n\nNow that we have identified these components, let\u0027s analyze each component separately. \n\nLet\u0027s start with the coffee shop. Is the coffee shop concept unique?",
"role": "user"
}
],
"raw_output_text_generation_0": "\u003cthinking\u003e\n\nThe user has presented a concept for a restaurant that combines a coffee shop, bookstore, and restaurant under one roof. To analyze this concept, we need to break it down into its individual components. \n\n\u003creflection\u003e\nIs the coffee shop concept unique?\nPossibilities: \n- No, it\u0027s a common concept.\n- Yes, it\u0027s unique because it\u0027s combined with other concepts.\n- The uniqueness is not in the coffee shop itself, but in the combination with other concepts.\n\nI choose the third option: The uniqueness is not in the coffee shop itself, but in the combination with other concepts.\n\u003c/reflection\u003e\n\n\u003creflection\u003e\nWhat makes the coffee shop component work in this concept?\nPossibilities: \n- The atmosphere.\n- The variety of coffee.\n- The connection with the restaurant and bookstore.\n- The destination of the month theme.\n\nI choose the third option: The connection with the restaurant and bookstore.\n\u003c/reflection\u003e\n\n\u003c/thinking\u003e\n\n\u003coutput\u003e\n\nThe coffee shop component is not unique by itself, but its connection with the restaurant and bookstore makes it an integral part of the overall concept. The combination of these three components creates a unique atmosphere and experience for customers. The coffee shop can serve as a hub where customers can relax and enjoy a coffee while browsing books or waiting for their food, which is inspired by the destination of the month."
},
"generation": "\u003cthinking\u003e\n\nThe user has presented a concept for a restaurant that combines a coffee shop, bookstore, and restaurant under one roof. To analyze this concept, we need to break it down into its individual components. \n\n\u003creflection\u003e\nIs the coffee shop concept unique?\nPossibilities: \n- No, it\u0027s a common concept.\n- Yes, it\u0027s unique because it\u0027s combined with other concepts.\n- The uniqueness is not in the coffee shop itself, but in the combination with other concepts.\n\nI choose the third option: The uniqueness is not in the coffee shop itself, but in the combination with other concepts.\n\u003c/reflection\u003e\n\n\u003creflection\u003e\nWhat makes the coffee shop component work in this concept?\nPossibilities: \n- The atmosphere.\n- The variety of coffee.\n- The connection with the restaurant and bookstore.\n- The destination of the month theme.\n\nI choose the third option: The connection with the restaurant and bookstore.\n\u003c/reflection\u003e\n\n\u003c/thinking\u003e\n\n\u003coutput\u003e\n\nThe coffee shop component is not unique by itself, but its connection with the restaurant and bookstore makes it an integral part of the overall concept. The combination of these three components creates a unique atmosphere and experience for customers. The coffee shop can serve as a hub where customers can relax and enjoy a coffee while browsing books or waiting for their food, which is inspired by the destination of the month.",
"instruction": "I have an idea for a new type of restaurant concept. It\u0027s a combination of a coffee shop, bookstore, and restaurant all under one roof. The restaurant would serve food inspired by the world\u0027s cuisines and have a \"destination of the month\" theme, where each month the restaurant would highlight a different country from around the world and the menu would change to reflect the cuisine of that country. The bookstore would have a selection of books that are relevant to the destination of the month. \n\nWhat do you think of the concept?\n\nTo analyze this concept, let\u0027s break it down into different components. What are the different components that make up this restaurant concept? \n\nThis concept is made up of several components: \n1) A coffee shop\n2) A bookstore\n3) A restaurant \n4) The \"destination of the month\" theme\n5) the selection of books relevant to the destination of the month\n\nNow that we have identified these components, let\u0027s analyze each component separately. \n\nLet\u0027s start with the coffee shop. Is the coffee shop concept unique?",
"model_name": "meta-llama/Meta-Llama-3.1-70B-Instruct"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("gabrielmbmb/distilabel-reflection-tuning", "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("gabrielmbmb/distilabel-reflection-tuning")
```

</details>

## References

```
@misc{xu2024magpiealignmentdatasynthesis,
title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing},
author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin},
year={2024},
eprint={2406.08464},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2406.08464},
}
```

<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="基于Distilabel构建" width="200" height="32"/>
</a>
</p>

# distilabel-reflection-tuning 数据集卡片

本数据集由distilabel创建，其官方地址为<https://distilabel.argilla.io/>。

为便于复现本数据集，已上传管道脚本：[reflection.py](https://huggingface.co/datasets/gabrielmbmb/distilabel-reflection-tuning/raw/main/reflection.py)。

可通过命令行接口（Command Line Interface，CLI）直接运行该脚本：
console
distilabel pipeline run --script "https://huggingface.co/datasets/gabrielmbmb/distilabel-reflection-tuning/raw/main/reflection.py"

## 数据集概览

本数据集包含一个`pipeline.yaml`文件，可通过distilabel的CLI复现生成该数据集的管道，或查看配置信息：
console
distilabel pipeline run --config "https://huggingface.co/datasets/gabrielmbmb/distilabel-reflection-tuning/raw/main/pipeline.yaml"

或查看配置详情：
console
distilabel pipeline info --config "https://huggingface.co/datasets/gabrielmbmb/distilabel-reflection-tuning/raw/main/pipeline.yaml"

## 数据集结构

每种配置下的示例数据结构如下：

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

json
{
"distilabel_metadata": {
"raw_input_text_generation_0": [
{
"content": "You're an AI assistant that responds the user with maximum accuracy. To do so, your first will think what the user is asking for, thinking step by step. During this thinking phase, you will have reflections that will help you clarifying ambiguities. In each reflection you will list the possibilities and finally choose one. Between reflections, you can think again. At the end of the thinking, you must draw a conclusion. You only need to generate the minimum text that will help you generating a better output, don't be verbose while thinking. Finally, you will generate an output based on the previous thinking.

This is the output format you have to follow:

<thinking>

Here you will think about what the user asked for.

<reflection>
This is a reflection.
</reflection>

<reflection>
This is another reflection.
</reflection>

</thinking>

<output>

Here you will include the output

</output>

",
"role": "system"
},
{
"content": "I have an idea for a new type of restaurant concept. It's a combination of a coffee shop, bookstore, and restaurant all under one roof. The restaurant would serve food inspired by the world's cuisines and have a "destination of the month" theme, where each month the restaurant would highlight a different country from around the world and the menu would change to reflect the cuisine of that country. The bookstore would have a selection of books that are relevant to the destination of the month.

What do you think of the concept?

To analyze this concept, let's break it down into different components. What are the different components that make up this restaurant concept?

This concept is made up of several components:
1) A coffee shop
2) A bookstore
3) A restaurant
4) The "destination of the month" theme
5) the selection of books relevant to the destination of the month

Now that we have identified these components, let's analyze each component separately.

Let's start with the coffee shop. Is the coffee shop concept unique?",
"role": "user"
}
],
"raw_output_text_generation_0": "<thinking>

The user has presented a concept for a restaurant that combines a coffee shop, bookstore, and restaurant under one roof. To analyze this concept, we need to break it down into its individual components.

<reflection>
Is the coffee shop concept unique?
Possibilities:
- No, it's a common concept.
- Yes, it's unique because it's combined with other concepts.
- The uniqueness is not in the coffee shop itself, but in the combination with other concepts.

I choose the third option: The uniqueness is not in the coffee shop itself, but in the combination with other concepts.
</reflection>

<reflection>
What makes the coffee shop component work in this concept?
Possibilities:
- The atmosphere.
- The variety of coffee.
- The connection with the restaurant and bookstore.
- The destination of the month theme.

I choose the third option: The connection with the restaurant and bookstore.
</reflection>

</thinking>

<output>

The coffee shop component is not unique by itself, but its connection with the restaurant and bookstore makes it an integral part of the overall concept. The combination of these three components creates a unique atmosphere and experience for customers. The coffee shop can serve as a hub where customers can relax and enjoy a coffee while browsing books or waiting for their food, which is inspired by the destination of the month."
},
"generation": "<thinking>

The user has presented a concept for a restaurant that combines a coffee shop, bookstore, and restaurant under one roof. To analyze this concept, we need to break it down into its individual components.

<reflection>
Is the coffee shop concept unique?
Possibilities:
- No, it's a common concept.
- Yes, it's unique because it's combined with other concepts.
- The uniqueness is not in the coffee shop itself, but in the combination with other concepts.

I choose the third option: The uniqueness is not in the coffee shop itself, but in the combination with other concepts.
</reflection>

<reflection>
What makes the coffee shop component work in this concept?
Possibilities:
- The atmosphere.
- The variety of coffee.
- The connection with the restaurant and bookstore.
- The destination of the month theme.

I choose the third option: The connection with the restaurant and bookstore.
</reflection>

</thinking>

<output>

The coffee shop component is not unique by itself, but its connection with the restaurant and bookstore makes it an integral part of the overall concept. The combination of these three components creates a unique atmosphere and experience for customers. The coffee shop can serve as a hub where customers can relax and enjoy a coffee while browsing books or waiting for their food, which is inspired by the destination of the month.",
"instruction": "I have an idea for a new type of restaurant concept. It's a combination of a coffee shop, bookstore, and restaurant all under one roof. The restaurant would serve food inspired by the world's cuisines and have a "destination of the month" theme, where each month the restaurant would highlight a different country from around the world and the menu would change to reflect the cuisine of that country. The bookstore would have a selection of books that are relevant to the destination of the month.

What do you think of the concept?

To analyze this concept, let's break it down into different components. What are the different components that make up this restaurant concept?

This concept is made up of several components:
1) A coffee shop
2) A bookstore
3) A restaurant
4) The "destination of the month" theme
5) the selection of books relevant to the destination of the month

Now that we have identified these components, let's analyze each component separately.

Let's start with the coffee shop. Is the coffee shop concept unique?",
"model_name": "meta-llama/Meta-Llama-3.1-70B-Instruct"
}

该配置子集可通过以下代码加载：
python
from datasets import load_dataset

ds = load_dataset("gabrielmbmb/distilabel-reflection-tuning", "default")

或由于仅存在一个名为`default`的配置，可简化加载方式如下：
python
from datasets import load_dataset

ds = load_dataset("gabrielmbmb/distilabel-reflection-tuning")

</details>

## 参考文献

@misc{xu2024magpiealignmentdatasynthesis,
title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing},
author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin},
year={2024},
eprint={2406.08464},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2406.08464},
}