fine-tuning-dataset-curator

license	Apache-2.0
name	fine-tuning-dataset-curator
description	Prepare high-quality datasets for LLM fine-tuning with filtering, deduplication, augmentation, and RLHF data formatting. Activate on: fine-tuning data, training data curation, RLHF dataset, data quality filtering, SFT dataset. NOT for: model training infrastructure (ai-engineer), prompt engineering without fine-tuning (prompt-engineer).
allowed-tools	Read,Write,Edit,Bash(python:*,pip:*,npm:*,npx:*)
category	AI & Machine Learning
tags	["fine-tuning","dataset-curation","rlhf","data-quality","sft"]
pairs-with	[{"skill":"ai-engineer","reason":"Fine-tuned models deploy through AI engineer pipelines"},{"skill":"data-pipeline-engineer","reason":"Large-scale data extraction and transformation before curation"},{"skill":"llm-evaluation-harness","reason":"Evaluate fine-tuned model quality on curated test sets"}]

Fine-Tuning Dataset Curator

Prepare, filter, deduplicate, and format high-quality datasets for supervised fine-tuning (SFT), RLHF, and DPO training of language models.

Activation Triggers

Activate on: "fine-tuning dataset", "training data preparation", "RLHF data", "DPO pairs", "SFT data", "data quality filtering", "dedup training data", "curate instruction dataset", "preference data"

NOT for: Model training loop implementation (ai-engineer), prompt optimization without fine-tuning (prompt-engineer), or general ETL pipelines (data-pipeline-engineer)

Quick Start

1. Define the task — What behavior should the fine-tuned model exhibit? Write 10 gold-standard examples by hand first.
2. Collect raw data — Scrape, export from logs, use existing datasets, or generate synthetic examples with a stronger model.
3. Filter and clean — Remove duplicates, low-quality entries, PII, and off-topic examples. Target quality over quantity.
4. Format for training — Convert to the target format: chat-ml for SFT, chosen/rejected pairs for DPO, reward signals for RLHF.
5. Validate — Hold out 10-15% for evaluation, verify distribution balance, run a small training test before full fine-tune.

Core Capabilities

Domain	Technologies	Notes
Quality Filtering	fasttext classifiers, perplexity scoring, regex rules	Remove noise before it poisons the model
Deduplication	MinHash (datasketch), exact hash, SimHash	Near-dedup critical for training stability
Augmentation	LLM-generated paraphrases, backtranslation, persona variation	3-5x dataset size with diversity
Format Conversion	chat-ml, Alpaca, ShareGPT, OpenAI JSONL	Match target training framework
PII Removal	presidio, regex, spaCy NER	Legal requirement for most training data
RLHF/DPO Prep	Preference pair generation, reward model labeling	Chosen/rejected pairs with margin scoring

Architecture Patterns

Pattern 1: SFT Data Curation Pipeline

Raw Sources ──→ [Extract] ──→ [Filter] ──→ [Dedup] ──→ [Augment] ──→ [Format] ──→ [Validate]
    │               │            │            │            │              │            │
  logs, docs    parse to     quality       MinHash      paraphrase    chat-ml     hold-out
  APIs, CSVs    instruction/ scoring       near-dedup   via LLM       or JSONL    eval set
                response     remove <                   persona                   distribution
                pairs        threshold                  variation                 check