Krutrim LLM: A Novel Tokenization Strategy for Multilingual Indic Languages with Petabyte-Scale Data Processing

Rahul Kumar; Shubham Kakde; Divyansh Rajput; Daud Ibrahim; Rishabh Nahata; Pidathala Sowjanya; Deepak Kumarr; Gautam Bhargava; Chandra Khatri

arXiv · 2407.12481

Krutrim LLM: A Novel Tokenization Strategy for Multilingual Indic Languages with Petabyte-Scale Data Processing

Rahul Kumar·Shubham Kakde·Divyansh Rajput·Daud Ibrahim·Rishabh Nahata·Pidathala Sowjanya·Deepak Kumarr·Gautam Bhargava·Chandra Khatri

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Abstract

We present a novel approach to data preparation for developing multilingual Indic large language model. Our meticulous data acquisition spans open-source and proprietary sources, including Common Crawl, Indic books, news articles, and Wikipedia, ensuring a diverse and rich linguistic representation. For each Indic language, we design a custom preprocessing pipeline to effectively eliminate redundant and low-quality text content. Additionally, we perform deduplication on Common Crawl data to address the redundancy present in 70% of the crawled web pages. This study focuses on developing high-quality data, optimizing tokenization for our multilingual dataset for Indic large language models with 3B and 7B parameters, engineered for superior performance in Indic languages. We introduce a novel multilingual tokenizer training strategy, demonstrating our custom-trained Indic tokenizer outperforms the state-of-the-art OpenAI Tiktoken tokenizer, achieving a superior token-to-word ratio for Indic languages.

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Rahul Kumar, Shubham Kakde, Divyansh Rajput, Daud Ibrahim, Rishabh Nahata, Pidathala Sowjanya, Deepak Kumarr, Gautam Bhargava, Chandra Khatri. 2024-07-17. Krutrim LLM: A Novel Tokenization Strategy for Multilingual Indic Languages with Petabyte-Scale Data Processing. https://arxiv.org/abs/2407.12481

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