RedPajama-10B-Weighted

A canonical 10 Billion token weighted subset of the RedPajama-Data-1T dataset.

Dataset Description

This dataset is a faithful reproduction of the original RedPajama-Data-1T distribution, scaled down to exactly 10 Billion tokens. It is designed to preserve the exact domain ratios of the original dataset (excluding the defunct 'Books' subset). This allows researchers and developers to prototype, debug, and test on a representative slice of the data without needing to download or process the full 1 Terabyte dataset.

It serves as both a standalone dataset for medium-scale experiments and the parent source for smaller slices (like the 1B subset).

Dataset Details

• Total Tokens: ~10,000,000,000 (10 Billion)
• Source: togethercomputer/RedPajama-Data-1T
• Language: English
• Format: Apache Parquet
• Producer: Kris Bailey

Motivation

The original RedPajama dataset is a standard for open-source LLM training, but its size (1TB+) makes it unwieldy for quick iteration, debugging, or educational purposes. Randomly sampling without care can destroy the delicate balance of data sources (CommonCrawl vs. C4 vs. GitHub).

This RedPajama 10B subset solves this by using a weighted interleaving strategy that strictly adheres to the original mixing ratios. It ensures that even at a smaller scale, the data seen by the model is distributionally equivalent to the full run.

Dataset Creation Process

The creation process involved a precise streaming and interleaving pipeline:

1. Source Streaming

We streamed the data directly from togethercomputer/RedPajama-Data-1T to avoid local storage bottlenecks.

2. Weighted Interleaving

We defined the target probabilities based on the original token counts:

• CommonCrawl: 74.16%
• C4: 14.78%
• GitHub: 4.98%
• ArXiv: 2.36%
• Wikipedia: 2.03%
• StackExchange: 1.69%

An interleaving algorithm sampled from these streams according to these probabilities to construct a single, unified stream.

3. Buffer Shuffling

To avoid burstiness (e.g., seeing 1000 Wikipedia articles in a row), we implemented a buffer shuffle with a size of 10,000 documents. This ensures a healthy mixture of domains throughout the dataset.

4. Verification

The process ran until exactly 10 Billion tokens were collected. We verified that the final composition matches the target weights.

Composition

Subset	Weight	Approx. Tokens
CommonCrawl	74.16%	~7.42 B
C4	14.78%	~1.48 B
GitHub	4.98%	~0.50 B
ArXiv	2.36%	~0.24 B
Wikipedia	2.03%	~0.20 B
StackExchange	1.69%	~0.17 B

Usage

from datasets import load_dataset

# Load the 10B weighted subset
ds = load_dataset("krisbailey/RedPajama-10B-Weighted", split="train")

print(ds)

Citation

If you use this dataset, please cite the original RedPajama work:

@software{together2023redpajama,
  author = {Together Computer},
  title = {RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset},
  month = April,
  year = 2023,
  url = {https://github.com/togethercomputer/RedPajama-Data}
}