# Data Processing Nemo Curator

GPU-accelerated data curation for LLM training.

- Repository: https://github.com/davila7/claude-code-templates (33041 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add davila7/claude-code-templates --skill data-processing-nemo-curator -g -a claude-code -y`
Codex: `npx skills add davila7/claude-code-templates --skill data-processing-nemo-curator -g -a codex -y`
Cursor: `npx skills add davila7/claude-code-templates --skill data-processing-nemo-curator -g -a cursor -y`
Gemini CLI: `npx skills add davila7/claude-code-templates --skill data-processing-nemo-curator -g -a gemini-cli -y`

## Description

GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# NeMo Curator - GPU-Accelerated Data Curation

NVIDIA's toolkit for preparing high-quality training data for LLMs.

## When to use NeMo Curator

**Use NeMo Curator when:**
- Preparing LLM training data from web scrapes (Common Crawl)
- Need fast deduplication (16× faster than CPU)
- Curating multi-modal datasets (text, images, video, audio)
- Filtering low-quality or toxic content
- Scaling data processing across GPU cluster

**Performance**:
- **16× faster** fuzzy deduplication (8TB RedPajama v2)
- **40% lower TCO** vs CPU alternatives
- **Near-linear scaling** across GPU nodes

**Use alternatives instead**:
- **datatrove**: CPU-based, open-source data processing
- **dolma**: Allen AI's data toolkit
- **Ray Data**: General ML data processing (no curation focus)

## Quick start

### Installation

```bash
# Text curation (CUDA 12)
uv pip install "nemo-curator[text_cuda12]"

# All modalities
uv pip install "nemo-curator[all_cuda12]"

# CPU-only (slower)
uv pip install "nemo-curator[cpu]"
```

### Basic text curation pipeline

```python
from nemo_curator import ScoreFilter, Modify
from nemo_curator.datasets import DocumentDataset
import pandas as pd

# Load data
df = pd.DataFrame({"text": ["Good document", "Bad doc", "Excellent text"]})
dataset = DocumentDataset(df)

# Quality filtering
def quality_score(doc):
    return len(doc["text"].split()) > 5  # Filter short docs

filtered = ScoreFilter(quality_score)(dataset)

# Deduplication
from nemo_curator.modules import ExactDuplicates
deduped = ExactDuplicates()(filtered)

# Save
deduped.to_parquet("curated_data/")
```

## Data curation pipeline

### Stage 1: Quality filtering

```python
from nemo_curator.filters import (
    WordCountFilter,
    RepeatedLinesFilter,
    UrlRatioFilter,
    NonAlphaNumericFilter
)

# Apply 30+ heuristic filters
from nemo_curator import ScoreFilter

# Word count filter
dataset = dataset.filter(WordCountFilter(min_words=50, max_words=100000))

The rest: https://github.com/davila7/claude-code-templates/blob/HEAD/cli-tool/components/skills/ai-research/data-processing-nemo-curator/SKILL.md