Demeter-LongCoT-6M


# **Demeter-LongCoT-6M**

> **Demeter-LongCoT-6M** is a high-quality, compact chain-of-thought reasoning dataset curated for tasks in mathematics, science, and coding. While the dataset spans diverse domains, it is primarily driven by mathematical reasoning, reflecting a major share of math-focused prompts and long-form logical solutions.

## Quick Start with Hugging Face Datasets🤗

```py
pip install -U datasets
```

```py
from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Demeter-LongCoT-6M", split="train")
```

## Overview

* **Total Samples**: \~6,443,748
* **Split**: `train` only
* **Languages**: English
* **Format**: Apache Arrow (auto-converted to Parquet)
* **License**: Apache-2.0
* **Tags**: `math`, `code`, `science`, `reasoning`, `longcot`

## Highlights

* Structured to promote **long-form, step-by-step reasoning**, ideal for training and evaluating chain-of-thought (CoT) capable models.
* Reasoning traces include natural, human-like explanations for both simple and complex problems.
* Fine-tuned across math word problems, logic-based questions, and technical prompts from STEM domains.

## Dataset Structure

Each entry in the dataset includes:

* **`problem`** (string): A math, science, or code problem.
* **`solution`** (string): A detailed step-by-step solution crafted in a long-form reasoning style.

The reasoning structure in solutions helps models understand logical flow, intermediate steps, and layered deductions—making this dataset suitable for advanced LLMs requiring interpretable outputs.

## Source & Derivation

Demeter-LongCoT-6M is a derivative collection synthesized and optimized from:

* A custom internal modular dataset tailored for logical and numeric reasoning tasks.
* Chain-of-thought style responses generated with QwQ 32B-based models, carefully filtered and structured for quality.

This dataset was created with a focus on enhancing CoT capabilities in large-scale models working on math, science, and code.

## License

Apache License 2.0

# **Demeter-LongCoT-6M**
> Demeter-LongCoT-6M 是一款高质量、轻量化的思维链（chain-of-thought, CoT）推理数据集，专为数学、科学与编码领域的任务打造。尽管该数据集覆盖多元应用领域，但其核心聚焦数学推理，包含大量以数学为主题的提示词与长格式逻辑解题方案。

## 基于 Hugging Face Datasets🤗 的快速上手指南
py
pip install -U datasets

py
from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Demeter-LongCoT-6M", split="train")

## 数据集概览
* **总样本量**：约6,443,748
* **数据划分**：仅包含训练集（train）
* **语言**：英语
* **格式**：Apache Arrow（自动转换为Parquet格式）
* **许可证**：Apache-2.0
* **标签**：`数学（math）`、`代码（code）`、`科学（science）`、`推理（reasoning）`、`长思维链（longcot）`

## 数据集亮点
* 采用结构化设计，支持**长格式、分步式推理**，非常适合训练和评估具备思维链（CoT）能力的模型。
* 推理轨迹包含针对简单与复杂问题的自然类人解释。
* 覆盖数学应用题、逻辑类问题以及科学、技术、工程与数学（Science, Technology, Engineering, Mathematics, STEM）领域的技术提示词，并经过微调适配。

## 数据集结构
该数据集的每条数据包含以下字段：
* **`problem`**（字符串类型）：数学、科学或编码类问题。
* **`solution`**（字符串类型）：以长格式推理风格撰写的详细分步解题方案。
解决方案中的推理结构可帮助模型理解逻辑流程、中间步骤与分层演绎过程，因此该数据集适用于需要可解释输出的高级大语言模型（Large Language Model, LLM）。

## 来源与衍生说明
Demeter-LongCoT-6M 是从以下资源中合成并优化得到的衍生数据集：
* 为逻辑与数值推理任务定制的专属内部模块化数据集。
* 基于 QwQ 32B 模型生成的思维链风格回复，经过严格筛选与结构化处理以保证数据质量。
本数据集的构建目标是增强面向数学、科学与编码任务的大规模模型的思维链能力。

## 许可证
Apache License 2.0