Sourcing LLM Training Text Data | Generated by AI

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How to Get Large Text Data for Training Language Models

Training large language models (LLMs) requires massive amounts of diverse, high-quality text data—often trillions of tokens from books, websites, code, and more. The key challenges are scale (terabytes to petabytes), quality (filtering noise, duplicates, and low-value content), and legality (respect copyrights, use public domain or licensed data). Here’s a step-by-step guide to sourcing it:

1. Start with Public Web Crawls: These are the backbone of most LLM training. They capture snapshots of the internet.
   • Filter for clean text using tools like CC-Net or Dedup (Python libraries via Hugging Face).
   • Process in chunks to handle size—use cloud storage (e.g., AWS S3) for downloads.
2. Use Curated Datasets: Pre-filtered collections from research groups. Download via APIs or direct links.
   • Focus on multilingual, domain-specific (e.g., code, science) subsets to match your needs.
   • Tools like Hugging Face Datasets library make loading easy: from datasets import load_dataset.
3. Supplement with Domain-Specific Sources:
   • Books: Project Gutenberg (public domain).
   • Wikipedia: Language dumps.
   • Code: GitHub archives (via BigCode).
   • Generate synthetic data: Use existing models (e.g., via OpenAI API) to create reasoning chains, but clean it to avoid contamination.
4. Legal and Ethical Tips:
   • Stick to open licenses (e.g., CC-BY, MIT).
   • Deduplicate (tools like MinHash) and remove PII (personal info).
   • For custom training, start small (e.g., fine-tune on 1-10GB) before scaling.
   • Compute costs: Expect 100s of GPU-hours for even modest training; use Colab or RunPod for testing.
5. Processing Pipeline:
   • Download → Clean (remove HTML, non-text) → Tokenize (e.g., with TikToken) → Train.
   • Libraries: Pandas for sampling, spaCy/NLTK for preprocessing.

Public datasets are free and massive—ideal for hobbyists or researchers. For production, companies often license proprietary data.

Training Data Sources for Specific Models

Proprietary models like those from OpenAI, Anthropic, and DeepSeek keep exact recipes secret for competitive reasons, but they’ve shared high-level details via papers, blogs, and leaks. Open-source models (e.g., Llama, Mistral) are more transparent, often releasing dataset blueprints.

• OpenAI’s GPT Models (e.g., GPT-4o): They train on a mix of publicly available internet data (filtered web crawls), books, articles, and code. Early GPTs used Common Crawl heavily; later ones emphasize high-quality STEM/coding sources. Total: Trillions of tokens, with heavy deduplication. They also incorporate licensed data and user interactions (with opt-outs). No full public release, but it’s “the entire internet” in essence—scraped, filtered, and augmented.

• Anthropic’s Models (e.g., Claude 3.5): Focus on safe, helpful data: Public web text, books, and synthetic examples generated for alignment (e.g., Constitutional AI). They use user chats from Claude (opt-out available) and RLHF datasets like HH-RLHF. Emphasis on diverse, non-toxic sources; some controversy over scraped YouTube transcripts. Total scale: Similar trillions, but more curated for ethics.

• DeepSeek Models (e.g., DeepSeek-V3, R1): Chinese open-ish models using plain web pages, e-books, and code repos. V3 pre-trained on 14.8T tokens without deliberate synthetic data, but R1 adds 600K synthetic reasoning samples via rejection sampling (generated by prior models). Sources: Web crawls + technical docs; proprietary mix, but transparent in papers.

• Open-Source Models (e.g., Llama 3, BLOOM, GPT-J): These explicitly use public datasets like The Pile (800GB multilingual mix), C4 (Colossal Clean Crawled Corpus, 750GB English web), or OSCAR (multilingual Common Crawl). BLOOM used ROOTS (1.6TB, 46 languages). They avoid proprietary data, focusing on reproducibility—check model cards on Hugging Face for exact breakdowns.

In short: All rely on web-scale data, but proprietaries add filtering/licensing/synthetics for quality. Open-source leans on community-curated publics.

Download Links for Large Public Text Datasets

Here are top free, downloadable sources (sizes approximate; check for updates). Start with subsets if storage is limited.

• Common Crawl: Monthly web snapshots (petabytes total). Filter with CC-MAIN indexes. Common Crawl Archives
• The Pile: 800GB diverse English text (books, code, arXiv, etc.). EleutherAI The Pile on Hugging Face
• C4 (Colossal Clean Crawled Corpus): 750GB cleaned English web (used for T5/GPT). TensorFlow Datasets C4
• OSCAR (Open Super-large Crawled Aggregated coRpus): Multilingual web (22 languages, ~10TB). OSCAR on Hugging Face
• Wikipedia Dumps: Full text extracts (English: ~20GB). Wikimedia Downloads
• BooksCorpus/OpenWebText: 11GB books + 40GB Reddit/web (GPT-2 era). OpenWebText on GitHub
• RedPajama: 1T+ tokens, replicated from Llama papers. TogetherAI RedPajama on HF
• LLMDataHub: Curated list of 100+ datasets (chat, code, etc.). GitHub LLMDataHub

For more, browse Hugging Face Datasets hub: Hugging Face Datasets. Always verify licenses!

References
How ChatGPT and our foundation models are developed
What GPT-oss Leaks About OpenAI’s Training Data
Anthropic Will Use Claude Chats for Training Data
Anthropic’s HH-RLHF Dataset
What went into training DeepSeek-R1?
The Complete Guide to DeepSeek Models
LLMDataHub: Awesome Datasets for LLM Training
Open-Sourced Training Datasets for LLMs
LLM Training Data: The 8 Main Public Data Sources

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