reasoning-1-1k

# Reasoning-1 1K

## Short about

This dataset will help in SFT training of LLM on the Alpaca format.

The goal of the dataset: to teach LLM to reason and analyze its mistakes using SFT training.

The size of 1.15K is quite small, so for effective training on SFTTrainer set *4-6* epochs instead of *1-3*.

*Made by Fluently Team ([@ehristoforu](https://huggingface.co/ehristoforu)) using [distilabel](https://github.com/argilla-io/distilabel) with love🥰*

## Dataset structure

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("fluently-sets/reasoning-1-1k", "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("fluently-sets/reasoning-1-1k")
```

<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>

# Reasoning-1 1K

## 数据集简介
本数据集可用于阿尔帕卡（Alpaca）格式下的大语言模型（Large Language Model，LLM）监督微调（Supervised Fine-Tuning，SFT）训练。
本数据集的目标为：通过监督微调训练，使大语言模型学会推理并分析自身的错误。
本数据集规模为1.15K，体量较小，因此若使用SFT训练器（SFTTrainer）开展高效训练时，请将训练轮次设置为**4-6轮**，而非**1-3轮**。
本数据集由Fluently团队（[@ehristoforu](https://huggingface.co/ehristoforu)）使用distilabel工具（https://github.com/argilla-io/distilabel）精心制作🥰

## 数据集结构
本子集可通过以下方式加载：
python
from datasets import load_dataset

ds = load_dataset("fluently-sets/reasoning-1-1k", "default")

或直接按如下方式加载，因该数据集仅有一种名为`default`的配置：
python
from datasets import load_dataset

ds = load_dataset("fluently-sets/reasoning-1-1k")

<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>