prithivMLmods/LAP2-K-Think-v1.a

---
license: apache-2.0
task_categories:
- text-generation
- question-answering
language:
- en
tags:
- code-x
- think
- code
- math
- v1.a
- agent
size_categories:
- 100K<n<1M
---


# **LAP2-K-Think-v1.a**

> The **LAP2-K-Think-v1.a** dataset, curated by **prithivMLmods** and available on Hugging Face, is a specialized reasoning dataset focused on **coding-based mathematics**, algorithmic problem solving, and code-x style thinking. It features a macro-mixture of coding and math-related problems. This dataset contains approximately **257,110 rows** in **Parquet** format, enabling efficient storage and high-performance training. Each entry includes a challenging problem statement with a detailed reasoning-based solution, suitable for **training**, **fine-tuning**, and **evaluating advanced models** in coding intelligence and math reasoning.

## Quick Start with Hugging Face Datasets

```bash
pip install -U datasets
```

```python
from datasets import load_dataset

dataset = load_dataset("prithivMLmods/LAP2-K-Think-v1.a", split="train")
```

---

## Dataset Summary

| Feature | Details |
| ------------ | ---------------------------------------------- |
| **Rows** | ~257K |
| **Size[partial]** | ~2.23 GB |
| **Format** | Parquet |
| **Language** | English |
| **License** | Apache-2.0 |
| **Domains** | Code reasoning, algorithmic math, code-x tasks |

---

## Data Columns

* **problem**: Math-based coding or algorithmic challenge prompts
* **solution**: Step-by-step reasoning and code-aligned answers

---

## Data Sources

This version primarily aggregates:

* **Xen-Arc AI CodeX-2M-Thinking** [Small traces, depending on the specific problem] → Code-x style reasoning and algorithmic prompts
* **Custom math-coding problems** curated for structured logic alignment [prithivMLmods/Gargantua-R1-Wee](https://huggingface.co/datasets/prithivMLmods/Gargantua-R1-Wee)

## Why This Dataset?

* Excellent for **code-aware reasoning models**
* Provides **thought-traces** enabling procedural logic learning
* Great benchmark for:

* Coding assistants
* Math-focused LLMs
* Instruction-tuned reasoning models

## Intended Use Cases

* Fine-tuning LLMs for competitive programming tasks
* Training models on strong trace-based reasoning
* Automated tutoring systems focused on coding + math
* Evaluation of algorithmic understanding in AI agents

## Maintainer

| Maintained by | Last Updated |
| --------------------------------------------------------- | ------------ |
| **[prithivMLmods](https://huggingface.co/prithivMLmods)** | **Nov 2025** |

许可证：Apache-2.0
任务类别：
- 文本生成
- 问答
语言：
- 英语
标签：
- code-x
- 思维
- 编码
- 数学
- v1.a
- AI智能体
数据规模区间：
- 10万 < 数据条数 < 100万

---


# **LAP2-K-Think-v1.a**

> 本**LAP2-K-Think-v1.a**数据集由**prithivMLmods**整理并发布于Hugging Face平台，是一款专注于**基于编码的数学问题**、算法问题求解与**code-x风格思维**的专用推理数据集。该数据集由编码与数学相关问题的宏观混合集构成，共包含约**257110条数据**，采用**Parquet**格式存储，可实现高效存储与高性能训练。每条数据均包含带有详细推理式解决方案的挑战性问题，适用于**训练**、**微调**以及评估编码智能与数学推理领域的高级模型。

## Hugging Face 数据集快速上手

bash
pip install -U datasets

python
from datasets import load_dataset

dataset = load_dataset("prithivMLmods/LAP2-K-Think-v1.a", split="train")

---

## 数据集概览

| 特征项 | 详情 |
| ------------ | ---------------------------------------------- |
| **数据条数** | ~25.7万 |
| **数据体量（部分）** | ~2.23 GB |
| **存储格式** | Parquet |
| **语言** | 英语 |
| **许可证** | Apache-2.0 |
| **应用领域** | 编码推理、算法数学、code-x任务 |

---

## 数据字段

* **problem**：基于数学的编码或算法挑战提示词
* **solution**：逐步骤推理且与代码对齐的解决方案

---

## 数据来源

本版本主要整合了以下数据源：
* **Xen-Arc AI CodeX-2M-Thinking** [少量样本，视具体问题而定] → 采用code-x风格的推理与算法提示
* **定制化数学编码问题集**：源自[prithivMLmods/Gargantua-R1-Wee](https://huggingface.co/datasets/prithivMLmods/Gargantua-R1-Wee)，用于结构化逻辑对齐训练

---

## 本数据集的优势

* 适配**编码感知型推理模型**
* 提供**思维轨迹**，支持过程式逻辑学习
* 可作为以下场景的优质基准测试集：
* 编码助手
* 专注数学领域的大语言模型（Large Language Model，LLM）
* 经过指令微调的推理模型

---

## 适用场景

* 针对竞赛编程任务微调大语言模型
* 训练具备优秀轨迹式推理能力的模型
* 面向编码与数学领域的自动化辅导系统
* 评估AI智能体的算法理解能力

---

## 维护方

| 维护方 | 最后更新时间 |
| --------------------------------------------------------- | ------------ |
| **[prithivMLmods](https://huggingface.co/prithivMLmods)** | **2025年11月** |