WithinUsAI/seed_ai_150k_package

---
license: apache-2.0
tags:
- WithInUsAi
- agent
- Gss1147
---
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📘 Dataset Card: seed_ai_150k_package

---
license: apache-2.0
task_categories:
- text-generation
language:
- en
pretty_name: Seed AI 150K Recursive Training Package
size_categories:
- 100K<n<1M
tags:
- synthetic-data
- instruction-tuning
- reasoning
- system-design
- llm-training
- recursive-training
---

# 🧠 Seed AI 150K Recursive Training Package

## Dataset Overview

The **Seed AI 150K Recursive Training Package** is a large-scale synthetic instruction dataset designed to support **LLM fine-tuning for structured reasoning, system-level thinking, and iterative reasoning behaviors**.

It contains **150,000 training samples**, split into three conceptual reasoning layers:

- **Mindset Layer (50K)**
- **Mindframe Process Layer (50K)**
- **Recursive Reasoning Layer (50K)**

The dataset is designed to improve:
- structured reasoning consistency
- system-level abstraction ability
- multi-step decomposition behavior
- reflective and iterative reasoning patterns

---

## 📊 Dataset Structure

Each record follows a simple instruction-response format:

```json
{
"instruction": "...",
"response": "...",
"metadata": {
"type": "mindset | mindframe | recursive_reasoning",
"domain": "...",
"index": 0,
"timestamp": "ISO-8601"
}
}

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🧩 Dataset Components

1. Mindset Layer (50K)

Focuses on:
• system-level thinking
• abstraction of real-world computing systems
• constraint-based reasoning

Example behavior:
• understanding systems as interacting components
• identifying inputs, outputs, and constraints

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2. Mindframe Process Layer (50K)

Focuses on:
• step-by-step reasoning
• structured decomposition
• analytical workflows

Encourages models to:
• break down complex systems
• follow ordered reasoning steps
• reduce unstructured responses

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3. Recursive Reasoning Layer (50K)

Focuses on:
• self-correction patterns
• iterative refinement of explanations
• multi-stage reasoning improvement

Important:
This does NOT create autonomous intelligence loops.
It teaches refinement-style reasoning patterns.

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🧠 Intended Use

This dataset is intended for:
• Supervised fine-tuning (SFT)
• Instruction tuning of LLMs
• Reasoning behavior improvement
• System design education tasks
• Synthetic data augmentation pipelines

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⚙️ Example Use Case

from datasets import load_dataset

dataset = load_dataset("GODsStrongestSoldier/seed_ai_150k_package")

print(dataset["train"][0])

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⚠️ Limitations

This dataset has important limitations:
• It is fully synthetic (not ground-truth factual data)
• It does not guarantee factual correctness
• It does not provide external verification sources (RAG not included)
• It does not produce autonomous or recursive intelligence
• Outputs reflect structured reasoning patterns, not real-world validation

Models trained on this dataset should be combined with:
• retrieval systems (RAG)
• factual verification pipelines
• evaluation benchmarks

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📦 Dataset Size
• Total samples: 150,000
• Format: JSONL
• Encoding: UTF-8
• Structure: instruction / response / metadata

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📚 Recommended Training Setup

For best results:
• Use supervised fine-tuning (SFT)
• Combine with real-world corpora (Wikipedia, arXiv)
• Add retrieval augmentation (RAG)
• Use evaluation filtering (truthfulness + reasoning score)

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📌 Citation

If you use this dataset, please cite:

@dataset{seedai150k,
title={Seed AI 150K Recursive Training Package},
author={GODsStrongestSoldier},
year={2026},
url={https://huggingface.co/datasets/GODsStrongestSoldier/seed_ai_150k_package}
}

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🚀 Final Note

This dataset is part of a broader research direction into:

structured reasoning augmentation for large language models

It is not a standalone intelligence system, but a training layer