---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- redpajama
- llm
- dataset-reproduction
- redpajama-1b
- redpajama-subset
- redpajama-weighted
- redpajama-sample
- natural-language-processing
size_categories:
- 100M<n<1B
pretty_name: RedPajama 1B Weighted Subset
---

# RedPajama-1B-Weighted

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

## Dataset Description

This is a strict, downsampled version of the [RedPajama-10B-Weighted](https://huggingface.co/datasets/krisbailey/RedPajama-10B-Weighted) dataset. It maintains the **exact domain distributions** of the full 1T dataset, resized to a lightweight **1 Billion token** footprint.

This dataset is ideal for:
- **Rapid Prototyping:** Train small models or debug pipelines in minutes rather than days.
- **Reference Baselines:** Use a standard, well-defined subset for comparative benchmarks.
- **Educational Use:** Explore the properties of large-scale pretraining data on consumer hardware.

## Dataset Details
- **Total Tokens:** ~1,000,000,000 (1 Billion)
- **Parent Source:** [krisbailey/RedPajama-10B-Weighted](https://huggingface.co/datasets/krisbailey/RedPajama-10B-Weighted)
- **Original Source:** [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T)
- **Language:** English
- **Format:** Apache Parquet
- **Producer:** Kris Bailey

## Motivation
While the 10B subset is manageable, sometimes you need something even faster. A 1 Billion token dataset is the "Goldilocks" size for many initial experiments—large enough to train a statistically significant small language model (e.g., TinyLlama size) but small enough to download and process on a laptop.

We created this by strictly downsampling the 10B dataset to ensure that the distribution remains consistent with the larger parent datasets.

## Dataset Creation Process

### 1. Source Selection
We utilized the `RedPajama-10B-Weighted` dataset as the source. This parent dataset was already constructed via weighted interleaving of the original RedPajama corpus.

### 2. Global Shuffling
The 10B dataset was globally shuffled (Seed: 43) to ensure that selecting the first $N$ tokens results in a random, representative sample, rather than a temporal slice.

### 3. Truncation
We selected the first **1 Billion tokens** from the shuffled stream.

### 4. Verification
We verified that the final subset retains the correct proportional mix of CommonCrawl, C4, GitHub, etc., matching the target distribution.

## Composition

| Subset | Weight | Approx. Tokens |
| :--- | :--- | :--- |
| **CommonCrawl** | 74.16% | ~741.6 M |
| **C4** | 14.78% | ~147.8 M |
| **GitHub** | 4.98% | ~49.8 M |
| **ArXiv** | 2.36% | ~23.6 M |
| **Wikipedia** | 2.03% | ~20.3 M |
| **StackExchange** | 1.69% | ~16.9 M |

## Usage
```python
from datasets import load_dataset

# Load the 1B weighted subset
ds = load_dataset("krisbailey/RedPajama-1B-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}
}
```