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Documentation

Build with Langtrain.

Guides and references for fine-tuning, evaluating and deploying open models on your own data.

Quick startAPI reference

Start here

  • What is LangtrainHow the platform works and why private fine-tuning matters.
  • Quick startInstall the SDK, prepare data, train with LoRA and deploy.
  • InstallationInstall on macOS, Linux or Windows and configure your API key.

By stage

  1. 01DataFormats, size and quality of training data.
  2. 02TrainLoRA, QLoRA, DoRA, SFT and preference tuning (DPO, ORPO, KTO).
  3. 03EvaluateScore a model on held-out data before you ship it.
  4. 04DeployServe a model on Langtrain Cloud or your own infrastructure.
  5. 05APIThe OpenAI-compatible REST API and SDKs.

All documentation

Get started

  • What is Langtrain
  • Quick start
  • Installation

Core concepts

  • Overview
  • Models
  • Datasets
  • Fine-tuning
  • Evaluation

Fine-tuning guides

  • Supervised fine-tuning (SFT)
  • LoRA & QLoRA
  • Preference tuning
  • Tutorial: chat models

Langtune

  • Fine-tune on your GPU
  • Local or cloud

Langvision

  • Fine-tune vision models

API and SDKs

  • REST API reference
  • Python SDK
  • Node.js and TypeScript

Deploy and own

  • Run your model anywhere
  • Security and compliance
  • Best practices

Resources

  • FAQ
  • Changelog

Can't find it? Read the FAQ, open an issue on GitHub, or contact us.