finetune-data-curator

name	finetune-data-curator
description	Web app for creating, editing, and validating JSONL fine-tuning datasets. Checks format compliance for OpenAI, Anthropic, and Llama formats, detects duplicates, scores quality, and exports clean datasets.
triggers	["fine-tune dataset","jsonl curator","training data","finetune data","dataset quality","llm training dataset","jsonl validation"]

finetune-data-curator Skill

When to Use

Use this skill when you need to:

• Create or curate JSONL datasets for LLM fine-tuning
• Validate dataset format compliance for OpenAI, Anthropic, or Llama training
• Find and remove near-duplicate samples from a dataset
• Score dataset quality and identify issues
• Split a dataset into train/eval subsets
• Convert datasets between OpenAI, Anthropic, and Llama formats
• Export cleaned datasets for training

Quick Start

cd finetune-data-curator
cp .env.example .env
# edit .env: set ADMIN_KEY=<secure random string> and DATA_DIR=./data

pnpm install
pnpm dev   # server + SPA on :4400

Open http://localhost:4400.

Docker Quick Start

ADMIN_KEY=mysecretkey docker compose up
# App: http://localhost:4400

Creating a Dataset

Via the web UI: click "New Dataset", choose a format (OpenAI, Anthropic, or Llama), name it.

Via API:

curl -X POST http://localhost:4400/api/datasets \
  -H "X-Admin-Key: $ADMIN_KEY" \
  -H "Content-Type: application/json" \
  -d '{"name": "my-dataset", "format": "openai"}'

Importing JSONL

Via the web UI: open a dataset, click Import, paste JSONL or upload a file.

Via API:

curl -X POST http://localhost:4400/api/datasets/ds_abc/samples \
  -H "X-Admin-Key: $ADMIN_KEY" \
  -H "Content-Type: application/json" \
  -d '[{"messages": [{"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi!"}]}]'

Running Validation

Via the web UI: open a dataset, click "Run Validation".

Via API:

curl -X POST http://localhost:4400/api/datasets/ds_abc/validate \
  -H "X-Admin-Key: $ADMIN_KEY"

Validation checks: format compliance, missing fields, empty content, excessive length, degenerate samples.

Finding Duplicates

curl -X POST http://localhost:4400/api/datasets/ds_abc/dedup \
  -H "X-Admin-Key: $ADMIN_KEY"

Returns pairs with Jaccard similarity above the configured threshold (default 0.8).

Splitting

curl -X POST http://localhost:4400/api/datasets/ds_abc/split \
  -H "X-Admin-Key: $ADMIN_KEY" \
  -H "Content-Type: application/json" \
  -d '{"ratio": 0.8, "seed": 42}'

Creates ds_abc.train.jsonl (80%) and ds_abc.eval.jsonl (20%).

Exporting

# Download as OpenAI format
curl "http://localhost:4400/api/datasets/ds_abc/export?format=openai" \
  -H "X-Admin-Key: $ADMIN_KEY" \
  -o my-dataset.jsonl

# Convert to Anthropic format on export
curl "http://localhost:4400/api/datasets/ds_abc/export?format=anthropic" \
  -H "X-Admin-Key: $ADMIN_KEY" \
  -o my-dataset-anthropic.jsonl

# Export train split only
curl "http://localhost:4400/api/datasets/ds_abc/export?format=openai&split=train" \
  -H "X-Admin-Key: $ADMIN_KEY" \
  -o train.jsonl

Environment Variables

Variable	Default	Description
PORT	4400	Server listen port
DATA_DIR	./data	Directory for JSONL files and index.json
ADMIN_KEY	(required)	Header value required for write operations
MAX_UPLOAD_MB	50	Maximum upload file size