A pipeline for dataset generation to finetune models.
Turn internal docs, policy notes, or even fine-tuning ideas from scratchinto a train-ready dataset through a simple 7-stage pipeline.
From idea to train-ready dataset
Finetuner guides your idea through a simple 7-stage pipeline:define, research, ground, generate, audit, export, and runbook.
3-phase dataset generation engine
SoTA models plan and judge. Low-cost executors models handcraft the rows.Judge models evaluate each batch of data.
Track dataset generation progress inside finetuner dashboard.
Track rows, source docs, costs, and quality gates as the pipeline runs.See what passed, what failed, and what is ready to export.
One license. Everything included.
No data-prep experts or dev team needed to scrape, clean, and format training data.Finetuner turns your ideas and docs into fine-tune-ready datasets.
Runs inside Claude Code, Codex, Cursor, or any terminal-first setup without changing how you work.
Bring private model API keys, open-source models, local models, or your own custom endpoints.
A 7-stage pipeline from raw idea, docs, or policy notes to a train-ready dataset.
Get current integrations plus future updates, new features, model adapters, and workflow improvements.
After generation starts, it runs on its own until the dataset is fully generated and audited.
Review progress, rows, docs, costs, quality gates, rejected examples, and exports in one place.
Questions before you build?
Simple answers about pricing, models, and how Finetuner works.
01What is finetuner.dev?
Finetuner.dev is a CLI workflow that turns raw data, internal docs, policies, examples or even just an idea into a train-ready dataset. Finetuner has a 7-stage pipeline that provides end-to-end dataset generation pipeline that removes all the friction from typical dataset prep. workflow.
02Why do we need finetuned models when LLMs are getting so good?
General LLMs are useful, but custom finetuned models can run on your own devices or servers, keep data private, reduce reliance on model providers, and outperform top LLMs on narrow industry-specific workflows.
03Is this a subscription?
No. It is a $199 one-year license, not a monthly plan. During the license window, you get the product, integrations, and updates without paying per seat or keeping a subscription alive.
04What is included?
You get the complete CLI workflow, custom built TUI (terminal UI), Browser dashboard, 15+ special skills. Plus, access to all updates, new features and integrations for next 1 year.
05Can I use my own models?
Yes. Bring hosted APIs, custom endpoints, private fine-tunes, or local models. Finetuner treats models as swappable seats for orchestration, row generation, and judging, so you can use the stack you already trust.
06Does it work with any CLI?
Yes. It is designed for terminal-first workflows and writes artifacts into your project. You can run it from your preferred shell, editor terminal, or automation wrapper without changing how your team works.
07Are model costs included?
No. Provider usage and local hardware are separate. You bring your own keys or local models, and Finetuner keeps the workflow explicit so budgets, retries, and audit passes stay under your control.
08Can it start from an idea?
Yes. The interview can turn a rough use case into a plan, dataset spec, style guide, rubric, and refusal policy. If you already have docs or examples, it freezes them into the source-of-truth pack instead of improvising or hallucinating.
09Why do we need 2 models (orchestrator and executor)?
Codex and Opus models are the best reasoning and planning models right now but they are expensive to generate dataset rows. That's why Codex or Opus plans and top chinese models (which are cheap) run as executors. This split strategy cuts the cost by 60%
10Who is this built for?
It is for builders and teams preparing fine-tuning datasets for open source models. The typical synthetic data generation pipelines are low quality, expensive and requires technical expertise. Finetuner.dev fixes all these 3 issues.
High quality dataset → High quality custom model
Finetune your local model on high quality dataset. Run the pipeline and get train-ready dataset. No technical knowledge required.