README.md

---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- redpajama
- llm
- dataset-reproduction
- redpajama-10b
- redpajama-subset
- redpajama-weighted
- redpajama-sample
- natural-language-processing
size_categories:
- 1B<n<10B
pretty_name: RedPajama 10B Weighted Subset
---

# RedPajama-10B-Weighted

A **canonical 10 Billion token weighted subset** of the [RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/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](https://huggingface.co/datasets/krisbailey/RedPajama-1B-Weighted)).

## Dataset Details
- **Total Tokens:** ~10,000,000,000 (10 Billion)
- **Source:** [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/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
```python
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:
```bibtex
@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}
}
```