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Documentation Index

Fetch the complete documentation index at: https://docs.arkor.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Quickstart

In a few minutes you will go from zero to a fine-tuned model you can chat with in a local Playground. The training itself takes 7 to 12 minutes; setup time depends on your connection and what is already installed.

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Prerequisites

  • Node.js 22.6 or newer. Node 24 is recommended.
  • pnpm. Other package managers (npm, yarn, bun) work too; the examples below use pnpm.
  • A working internet connection. Training runs on Arkor’s managed GPUs.
No account, no credit card, no GPU on your machine.

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1. Scaffold a project

pnpm create arkor my-arkor-app
cd my-arkor-app
The scaffolder asks which template you want. Pick the one closest to what you eventually want to build:
TemplateTaskOutput shapeEstimated training
triageSupport triage{ category, urgency, summary, nextAction }~7 min
translate9-language translation{ translation, detectedLanguage }~7 min
redactionPII redaction{ redactedText, redactedCount, tags }~12 min
Each template pairs the same small open-weight base (unsloth/gemma-4-E4B-it) with a curated public dataset on HuggingFace. The training is real and finishes in minutes, so you get to see the whole loop end to end. To skip the prompt: 
pnpm create arkor my-arkor-app --template triage

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2. Look at what was generated

my-arkor-app/
├── src/arkor/
│   ├── index.ts        # createArkor({ trainer })
│   └── trainer.ts      # createTrainer({ ... })
├── arkor.config.ts
├── .arkor/             # state + build artifacts (gitignored)
└── package.json        # dev / build / start
The two files that matter for now:
  • src/arkor/trainer.ts holds your trainer definition. Model, dataset, LoRA settings, hyperparameters, and lifecycle callbacks all live here.
  • src/arkor/index.ts is the entry point. It calls createArkor({ trainer }) so the CLI and Studio can discover what to run.
Open trainer.ts. The shape is the same across templates; only the dataset, output schema, and example prompts differ.

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3. Open Studio and start a run

pnpm dev
This runs arkor dev, which boots Studio at http://localhost:4000. Studio is the UI; arkor dev itself does not start training and does not watch your trainer files. In the browser, click Run training. Studio runs arkor start for you in the background, which submits the job to the managed backend using .arkor/build/index.mjs. If that artifact is missing, arkor start auto-builds it from src/arkor/index.ts; in the Studio flow you rarely see this path because Studio’s /api/manifest endpoint already rebuilt the artifact when the Run training page loaded. The first time you trigger anything, an anonymous workspace is created automatically: no signup, no credit card. Once a run is in flight, three views matter:
  • Jobs. A list of training runs. Click into one to see live status.
  • Loss chart and event log. As the run streams from the managed GPU, the loss curve updates and the log tail shows training events. The first run takes 7 to 12 minutes depending on the template.
  • Playground. After a job completes, pick the final adapter from the selector and chat with it. Use the mode toggle to switch between the base model and the adapter. To run inference on intermediate checkpoints while a run is still in flight, use onCheckpoint callbacks instead of Studio.
If you edit src/arkor/ between runs, run npx arkor build (or refresh Studio’s Run training page so its manifest endpoint rebuilds the artifact) before the next click. arkor start reuses the existing build artifact between clicks otherwise, so the new edits would not flow through.

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4. Claim your work (optional)

Anonymous workspaces are useful for trying things out, but the work is tied to that local machine. Once you decide you want to keep what you trained: 
npx arkor login
The scaffolded project installs arkor as a local devDependency, so prefer npx arkor <command> (or pnpm exec arkor <command>) over a bare arkor invocation unless you have installed the CLI globally. The generated project README uses the same convention. This runs an Auth0 PKCE flow on a loopback port and links your local credentials to an account. From then on, runs from this machine show up under that account on any device.

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5. Where to go next

  • Concepts. Read Concepts to build a mental model of createArkor, createTrainer, the lifecycle callbacks, and Studio.
  • Customize the trainer. Open src/arkor/trainer.ts and tweak lora.r, maxSteps, or add more callbacks. Run npx arkor build (or refresh Studio’s Run training page) before the next click so your edits land.
  • Try another template. Run pnpm create arkor again with a different --template to compare.