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>

搜集汇总

背景与挑战

背景概述

该数据集用于基于Alpaca格式的LLM SFT训练，旨在提升模型的推理和错误分析能力。数据规模较小（1.15K），建议设置4-6个epochs以实现有效训练。