Together.ai has released RedPajama-V2, a dataset with 30 trillion tokens that can be used for training large language models (LLMs). RedPajama-1T, a 5TB dataset, was released earlier this year. The researchers believe that RedPajama-V2 will provide a foundation for high-quality datasets for LLM training and in-depth study. The dataset includes annotations and deduplication clusters. The team also plans to expand the set of high-quality annotations in the future.
Together AI Releases RedPajama v2: An Open Dataset with 30 Trillion Tokens for Training Large Language Models
High-quality data is crucial for the success of advanced language models like Llama, Mistral, Falcon, MPT, and RedPajama. However, obtaining refined data for training these models can be challenging due to various factors such as low-quality sources and biases in web content. Gathering the right dataset requires significant time, resources, and money. To address this issue, Together AI has released RedPajama v2, a vast dataset with 30 trillion tokens, making it the largest publicly available dataset for language model training.
Key Features of RedPajama v2:
- 30 trillion high-quality English tokens
- 84 processed dumps from CommonCrawl
- 40+ quality annotations for data filtering
- Deduplication clusters to eliminate duplicates
RedPajama v2 is built from 84 CommonCrawl crawls and other publicly available web data. The dataset includes raw text, quality annotations, and deduplication clusters. Researchers have computed over 40 popular quality annotations for the text documents, allowing model developers to filter and reweight the dataset according to their needs. The dataset also undergoes deduplication using minhash signatures and Bloom filters.
With 113 billion documents in English, German, French, Spanish, and Italian, RedPajama v2 provides a solid foundation for extracting high-quality datasets for language model training. The dataset has been reduced by 40% after deduplication, but the number of documents in the tail partition remains significant.
Together AI plans to expand the set of high-quality annotations in the future, including contamination annotations, topic modeling, and categorization annotations. They encourage the community to contribute to this initiative.
To learn more about RedPajama v2, you can visit their Github and Reference Blog.
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If you want to stay competitive and leverage AI to redefine your way of work, Together AI’s RedPajama v2 dataset can be a valuable resource. Here are some practical steps to consider:
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If you need guidance on AI KPI management or want continuous insights into leveraging AI, you can connect with us at hello@itinai.com. Stay updated on the latest AI research news and projects by following our Telegram channel or Twitter @itinaicom.
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