nemo-curator

You can accelerate LLM training data curation using GPU-accelerated pipelines that perform fuzzy deduplication, quality filtering, and PII redaction up to 16x faster than CPU alternatives. This leverages RAPIDS, Dask, and Ray-based orchestration for scalable processing.

GPU-accelerated data curation uses RAPIDS and cuDF to process multi-modal datasets, performing fuzzy deduplication, quality filtering, and PII redaction. It ensures high-quality inputs for LLM training by leveraging Ray-based pipeline orchestration for scalable data processing.

Yes, this skill supports multi-modal data curation for text, image, video, and audio datasets using specialized pipelines. It applies GPU-accelerated filtering, deduplication, and PII redaction across these formats to ensure high-quality model inputs.

The best way to prepare Common Crawl data is to use a GPU-accelerated curation pipeline that removes exact and fuzzy duplicates, filters low-quality or toxic content, and redacts sensitive PII. This leverages RAPIDS and Dask for scalable, high-quality LLM training inputs.

Yes, RAPIDS, cuDF, and Dask are required dependencies for GPU-accelerated data curation. RAPIDS provides GPU-based performance for deduplication and filtering, while Dask and Ray handle scalable pipeline orchestration across massive LLM training datasets.