Deduplicate and filter large multimodal datasets for LLM training.
npx skills add https://github.com/AVOI-CEO/avoi-agent --skill nemo-curator-avoi-ceo
Please help me install this Agent Skill. Skill: nemo-curator Source: https://github.com/AVOI-CEO/avoi-agent/tree/main/optional-skills/mlops/nemo-curator Command: npx skills add https://github.com/AVOI-CEO/avoi-agent --skill nemo-curator-avoi-ceo
Nemo Curator provides GPU-accelerated data curation for large-scale multimodal datasets used in LLM training, enabling fast deduplication, quality filtering, PII redaction, and NSFW detection to improve data quality at scale.
Run Nemo Curator on a GPU-enabled cluster to begin a data curation pipeline that deduplicates, filters, redacts, and classifies a multimodal training dataset.
High-intent search queries and answers about installing and using this skill.
To prepare web scraped data for LLM training, apply quality filtering and deduplication to clean the raw corpora. This pipeline uses 30+ configurable quality filters to remove low-quality content before model training.
PII redaction and NSFW detection enforce data safety and compliance during curation. These features automatically identify, filter, and redact sensitive content across large multimodal datasets to ensure training corpora meet safety standards.
The best way to clean web scraped data for RedPajama style training corpora is using an automated curation pipeline. This approach applies GPU-accelerated quality filtering and deduplication to prepare massive multimodal training datasets efficiently.
Yes, GPU-accelerated data curation supports text, image, video, and audio sources. It applies uniform deduplication, quality filtering, and classification across multimodal web crawls to prepare comprehensive LLM training datasets.
mkurmanEnable GPU acceleration for Python workloads across CUDA-enabled libraries.
granietGenerate Axolotl YAML configurations for fine-tuning large language models.
JKhyroTrain PyTorch models with Lightning Trainer and LightningModule workflows.
SignmanalCurate large-scale LLM training datasets with GPU-accelerated filtering and deduplication.
