JaveyZou/KingDesign

---
size_categories: 1K<n<10K
tags:
- synthetic
- datadesigner
- king-design
- code
configs:
- config_name: data
data_files: data/*.parquet
default: true
license: mit
task_categories:
- question-answering
language:
- zh
---

<div style="display: flex; justify-content: space-between; align-items: flex-end; width: 100%; margin-bottom: 1rem;">
<h1 style="flex: 1; margin: 0;">Kingdesign</h1>
<sub style="white-space: nowrap;">Made with ❤️ using 🦥 Unsloth Studio</sub>
</div>

---

all was generated with Unsloth Recipe Studio. It contains 5,351 generated records.

---

## 🚀 Quick Start

```python
from datasets import load_dataset

# Load the main dataset
dataset = load_dataset("JaveyZou/KingDesign", "data", split="train")
df = dataset.to_pandas()

```

---

## 📊 Dataset Summary

- **📈 Records**: 5,351
- **📋 Columns**: 3

- **✅ Completion**: 89.2% (6,000 requested)

---

## 📋 Schema & Statistics

| Column | Type | Column Type | Unique (%) | Null (%) | Details |
|--------|------|-------------|------------|----------|---------|
| `llm_structured_1` | `dict` | llm-structured | 5329 (99.6%) | 0 (0.0%) | Tokens: 402 out / 828 in |

---

## ⚙️ Generation Details

Generated with 3 column configuration(s):

- **llm-structured**: 1 column(s)

- **seed-dataset**: 2 column(s)

📄 Full configuration available in [`builder_config.json`](builder_config.json) and detailed metadata in [`metadata.json`](metadata.json).

---

## 📚 Citation

If you use Data Designer in your work, please cite the project as follows:

```bibtex
@misc{nemo-data-designer,
author = {The NeMo Data Designer Team, NVIDIA},
title = {NeMo Data Designer: A framework for generating synthetic data from scratch or based on your own seed data},
howpublished = {\url{https://github.com/NVIDIA-NeMo/DataDesigner}},
year = 2026,
note = {GitHub Repository},
}
```

---

## 💡 About NeMo Data Designer

NeMo Data Designer is a general framework for generating high-quality synthetic data that goes beyond simple LLM prompting. It provides:

- **Diverse data generation** using statistical samplers, LLMs, or existing seed datasets
- **Relationship control** between fields with dependency-aware generation
- **Quality validation** with built-in Python, SQL, and custom local and remote validators
- **LLM-as-a-judge** scoring for quality assessment
- **Fast iteration** with preview mode before full-scale generation

For more information, visit: [https://github.com/NVIDIA-NeMo/DataDesigner](https://github.com/NVIDIA-NeMo/DataDesigner) (`pip install data-designer`)