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.
createTrainer
import { createTrainer } from "arkor";
export const trainer = createTrainer({
name: "support-bot-v1",
model: "unsloth/gemma-4-E4B-it",
dataset: { type: "huggingface", name: "arkorlab/triage-demo" },
lora: { r: 16, alpha: 16 },
maxSteps: 100,
});
createTrainer returns a Trainer object with start / wait / cancel methods (see Trainer control). The function does not run client-side validation on TrainerInput: TypeScript checks the typed fields at compile time, and the rest is forwarded to the cloud-api when start() runs. Bad values surface as backend errors during the run, not as client-side throws at construction.
Required fields
| Field | Type | Notes |
|---|
name | string | Run name, shown in Studio and on the managed backend. |
model | string | Base model identifier. Today the curated templates use unsloth/gemma-4-E4B-it. |
dataset | DatasetSource | HuggingFace dataset name or blob URL. |
Typed optional fields
These have first-class TypeScript types and are safe to use:
| Field | Type | Default | Effect |
|---|
lora | LoraConfig | backend default | LoRA / QLoRA knobs (r, alpha, maxLength?, loadIn4bit?). |
maxSteps | number | backend default | Cap on training steps. |
numTrainEpochs | number | backend default | Number of dataset passes (alternative to maxSteps). |
learningRate | number | backend default | Optimizer step size. |
batchSize | number | backend default | Per-device training batch size. |
optim | string | backend default | Optimizer name. |
lrSchedulerType | string | backend default | Learning-rate schedule (e.g. linear, cosine). |
weightDecay | number | backend default | Regularization weight. |
dryRun | boolean | false | Smoke test (see below). |
callbacks | Partial<TrainerCallbacks> | {} | See Lifecycle callbacks. |
abortSignal | AbortSignal | none | Stops a local wait(). Does not cancel the backend job; see Trainer control. |
LoraConfig
interface LoraConfig {
r: number; // LoRA rank (often 8 / 16 / 32)
alpha: number; // LoRA alpha (often 2 × r)
maxLength?: number; // Truncate samples beyond this many tokens
loadIn4bit?: boolean; // Load the base model in 4-bit (QLoRA)
}
lora entirely to take the backend defaults. r: 16, alpha: 16 is the starting point the bundled templates use.
dryRun
createTrainer({
name: "smoke",
model: "unsloth/gemma-4-E4B-it",
dataset: { type: "huggingface", name: "arkorlab/triage-demo" },
dryRun: true,
});
dryRun: true tells the backend to truncate the dataset and cap the number of steps so the run finishes in a couple of minutes while still exercising every stage of the pipeline (data load, chat-template render, training loop, checkpoint upload, event stream).
It still uses GPU time, just much less of it. Useful in CI or when wiring up callbacks for the first time, not as a way to avoid spend entirely.
Advanced: forwarded fields
These are typed as unknown and forwarded to the cloud API verbatim (packages/arkor/src/core/trainer.ts:100-108). They are reserved on the input shape so the SDK can support fields the backend exposes before the SDK has caught up with first-class typing.
| Field | Status |
|---|
warmupSteps, loggingSteps, saveSteps, evalSteps | Forwarded as-is. Use only if you already know the backend’s expected shape. |
trainOnResponsesOnly | Forwarded as-is. |
datasetFormat, datasetSplit | Forwarded as-is. The dedicated dataset field’s split should be preferred for HuggingFace sources. |
Not yet
- Multi-trainer projects.
createArkor accepts a single trainer; there is no array form. To register a second trainer, you would either swap the export at runtime or wait for the API to gain that shape.
