A Practical LLM Pretraining Pipeline with LanceDB

LanceDB proposes a unified, versioned table approach to language-model pretraining that retains raw text, curation metadata, token IDs, and token counts together rather than creating separate cleaned, tokenized, shuffled, and packed datasets at each stage. Its streaming dataloader applies filtering, global shuffling, and sequence packing at training time, minimizing padding and truncation while allowing changes to filters, tokenizers, seeds, or sequence lengths without rebuilding files. In tests on FineWeb-Edu data, the system processed 2.4 million documents into training-ready data in about 11 minutes and trained a 124M-parameter GPT-2 model on eight H100 GPUs in 14 minutes at 3.18 million tokens per second, with similar performance when reading directly from S3 in Norway. Comparisons found that LanceDB matched pre-packed and pre-shuffled alternatives while substantially outperforming random Parquet reads from S3, which suffered from inefficient row-group access. A larger experiment with 17.5 million documents and 18.06 billion tokens trained a 354M-parameter model on a 7B-token budget in 97 minutes, reaching a lower validation loss and more coherent output. Because the source data remains queryable throughout and after training, the approach also supports data inspection, attribution, contamination checks, and retrieval of documents associated with model behavior.

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