nemo-curator

GPU-accelerated data curation for LLM training automates deduplication across text, image, video, and audio datasets. It applies exact, fuzzy, and semantic deduplication techniques at scale using RAPIDS to clean large multimedia corpora.

Cleaning web data for LLMs uses over 30 quality filtering heuristics to remove low-quality or harmful content. This data curation process ensures your training corpora meet high standards by applying GPU-accelerated quality filters.

GPU-accelerated data curation includes built-in PII redaction and NSFW detection to protect privacy and safety. These features automatically identify and remove sensitive or unsafe content from large-scale multimodal training datasets.

Yes, data curation workflows achieve cross-GPU scaling using RAPIDS and Dask. This integration allows you to process large-scale LLM training datasets across multiple GPUs, significantly accelerating data cleaning and deduplication tasks.

GPU-accelerated data curation for LLM training supports Linux and macOS environments. It requires dependencies on nemo-curator, cudf, dask, and rapids to execute scalable data preparation and quality filtering workflows.

Fuzzy and semantic deduplication identifies and removes similar or contextually identical entries in text and image datasets. This GPU-accelerated process scales across multimodal data to ensure diverse and high-quality LLM training corpora.