dignity045/Collective-Corpus

🧠 Collective Corpus — Universal Pretraining + Finetuning Dataset (500B+ Tokens)

  

`Collective-Corpus` is a massive-scale, multi-domain dataset designed to train Transformer-based language models from scratch and finetune them across a wide variety of domains — all in one place.

📚 Dataset Scope

This dataset aims to cover the full LLM lifecycle, from raw pretraining to domain-specialized finetuning.

1. Pretraining Corpus

• —Large-scale, diverse multilingual text sources
• —Cleaned, deduplicated, and filtered for quality
• —Inspired by datasets like C4 and FineWeb

2. Domain-Specific Finetuning

• —Instruction Following & Dialogue — Chatbots, multi-turn conversations
• —Code — Python, JavaScript, Java, C++, and more
• —Math & Logical Reasoning
• —Specialized Fields — Research papers, technical documentation

📊 Scale

• —Total Tokens: 500B+
• —Estimated Text Samples: 700M+
• —Target Model Size: Suitable for training large models from scratch
• —Covers general-purpose and domain-specific training needs

🎯 Goals

1. 1.Build a unified corpus for full-stack LLM development.
2. 2.Enable open and reproducible large-scale language model research.
3. 3.Support finetuning for high-impact domains like code, math, and dialogue.

🚧 Current Status

• —Model Pretraining: Currently training a Transformer model from scratch on the full 500B+ token dataset.
• —Public Release: Planned after model training completes.

🤝 Collaboration

We are actively seeking open-source collaborators to:

• —Contribute to dataset cleaning, filtering, and deduplication
• —Assist in large-scale model training and evaluation
• —Provide expertise for specialized domain corpora

We also offer free guidance on:

• —Dataset curation best practices
• —Efficient large-scale LLM training pipelines
• —Transformer architecture optimization

💼 Open for Collaboration

I’m actively looking to connect with researchers, engineers, and organizations passionate about dataset engineering, large-scale model training, and applied NLP. Whether it’s open-source projects, research collaborations, or large-scale AI initiatives — let’s build something impactful together.

🔗 GitHub: Dhiraj309 🔗 LinkedIn: Dhiraj Patil

📅 Release Timeline

📜 License

Released under the Apache License 2.0 — you are free to use, modify, and distribute this dataset in compliance with the full license text.

🌍 Let’s build the next generation of open-source LLMs — together.