---
title: reasoning-1-1k
canonical_url: "https://www.modelscope.cn/datasets/AI-ModelScope/reasoning-1-1k"
md_url: "https://www.modelscope.cn/datasets/AI-ModelScope/reasoning-1-1k.md"
repository: AI-ModelScope/reasoning-1-1k
last_updated: 2024-12-29
license: "Apache License 2.0"
storage_size: "1.6 MB"
downloads: 1465
stars: 1
---

# reasoning-1-1k

> reasoning-1-1k - AI-ModelScope 在 ModelScope 开源的数据集。This dataset will help in SFT training of LLM on the Alpaca format.

AI-ModelScope/reasoning-1-1k 是 ModelScope 魔搭社区上的数据集，存储大小 1.6 MB，采用 Apache License 2.0 许可。

- **Repository**: AI-ModelScope/reasoning-1-1k
- **License**: Apache License 2.0
- **Storage size**: 1.6 MB
- **Downloads**: 1465
- **Stars**: 1
- **Last updated**: 2024-12-29

Source: https://www.modelscope.cn/datasets/AI-ModelScope/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")
```


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