ViolentlyPurple/Basic-GPT-AI-Dataset

Basic GPT AI Dataset

A curated collection of training data for GPT-style language models

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📋 Project Description

Basic GPT AI Dataset is a Hugging Face dataset repository containing pre-processed training data designed for fine-tuning and training GPT-style language models. The dataset is provided in a convenient Pickle format, making it easy to load and integrate into your PyTorch or TensorFlow pipelines.

The dataset is released under the Apache 2.0 license and is free to use for both academic and commercial purposes.

Note: As the dataset author states: "TBH i do not own any of these datasets, I create depending on my power over a certain Domain, Free To Use (FTS), Thank you :3"

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✨ Features

• 🧠 GPT-Ready Format – Pre-processed data structured for language model training
• 📦 Pickle Serialization – Easy to load with Python's pickle module
• 🚀 Large-Scale – Over 2.1 GB of training data across two compressed archives
• 📄 Apache 2.0 License – Permissive open-source licensing for maximum reusability
• 🔓 Free to Use – No restrictions on usage (commercial or academic)

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📦 Dataset Contents

The repository contains two main data files:

File	Size	Format	Description
Another Dataset because why not.zip	1.18 GB	Pickle (compressed)	Primary training data
Data.zip	947 MB	Pickle (compressed)	Supplemental training data

Both files contain Pickle serialized objects – typically lists, dictionaries, or arrays of text samples suitable for GPT training.

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🚀 Quick Start

Prerequisites

• Python 3.8+
• datasets library (optional, for Hugging Face integration)
• torch or tensorflow (depending on your framework)

Installation

# Install the Hugging Face datasets library
pip install datasets

# Or, if you prefer to download manually:
pip install requests tqdm

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💻 Usage Examples

Option 1: Load with Hugging Face datasets

from datasets import load_dataset

# Load the dataset (note: viewer may be unavailable, but you can still load the raw files)
dataset = load_dataset("ViolentlyPurple/Basic-GPT-AI-Dataset", split="train")

⚠️ Note: The dataset viewer may not be available due to configuration issues. If you encounter errors, use the manual loading method below.

Option 2: Manual Download & Load

import pickle
import zipfile
import requests
from pathlib import Path

# Download the zip file
url = "https://huggingface.co/datasets/ViolentlyPurple/Basic-GPT-AI-Dataset/resolve/main/Data.zip"
response = requests.get(url, stream=True)

# Save and extract
zip_path = Path("Data.zip")
with open(zip_path, "wb") as f:
    for chunk in response.iter_content(chunk_size=8192):
        f.write(chunk)

# Extract and load pickle
with zipfile.ZipFile(zip_path, "r") as zip_ref:
    zip_ref.extractall("data/")

# Load the pickle file(s)
with open("data/your_pickle_file.pkl", "rb") as f:
    data = pickle.load(f)

print(f"Loaded {len(data)} samples")

Option 3: Direct Download with wget

# Download using wget
wget https://huggingface.co/datasets/ViolentlyPurple/Basic-GPT-AI-Dataset/resolve/main/Data.zip
wget https://huggingface.co/datasets/ViolentlyPurple/Basic-GPT-AI-Dataset/resolve/main/Another%20Dataset%20because%20why%20not.zip

# Unzip
unzip Data.zip -d data/
unzip "Another Dataset because why not.zip" -d data/

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⚙️ Configuration

The dataset does not require extensive configuration. However, you may want to:

• Set a cache directory for Hugging Face datasets:

  import os
  os.environ["HF_DATASETS_CACHE"] = "/path/to/cache"
  

• Stream large files to avoid memory issues:

  dataset = load_dataset("ViolentlyPurple/Basic-GPT-AI-Dataset", streaming=True)
  

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🧪 Running Tests

To verify the dataset loads correctly:

import pickle
import zipfile

def test_dataset(zip_path):
    """Verify that the pickle file loads successfully."""
    with zipfile.ZipFile(zip_path, "r") as z:
        # Get the first pickle file
        pkl_files = [f for f in z.namelist() if f.endswith('.pkl')]
        if not pkl_files:
            print("❌ No pickle files found in archive")
            return False
        
        with z.open(pkl_files[0]) as f:
            try:
                data = pickle.load(f)
                print(f"✅ Successfully loaded {len(data)} items from {pkl_files[0]}")
                return True
            except Exception as e:
                print(f"❌ Failed to load pickle: {e}")
                return False

# Test both archives
test_dataset("Data.zip")
test_dataset("Another Dataset because why not.zip")

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📁 Project Structure

Basic-GPT-AI-Dataset/
├── README.md                          # This file
├── Data.zip                           # 947 MB – primary dataset
├── Another Dataset because why not.zip # 1.18 GB – supplemental dataset
└── .gitattributes                     # Git LFS configuration

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🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Report Issues – Open a discussion on the Hugging Face discussion board

2. Improve Documentation – Submit a PR with updates to this README or dataset card

3. Add Data – If you have relevant GPT training data, consider contributing

4. Fix the Dataset Viewer – The current viewer is experiencing issues. Help us configure it properly by following the dataset viewer configuration guide

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📄 License

This dataset is distributed under the Apache License 2.0.

Copyright 2026 ViolentlyPurple

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at:

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

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🙏 Acknowledgments

• Thanks to the open-source community for providing the tools and frameworks that make datasets like this possible
• Special thanks to the Hugging Face team for hosting and maintaining the platform

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📬 Contact & Support

• Dataset Page: Hugging Face
• Discussions: Open a discussion
• Issues: Report problems via the discussions tab

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