LoRA fine-tuning

Produce lightweight adapters from a pinned base model and instruction dataset.

fetch base model→prepare examples→fine-tune adapters→evaluate→publish adapterrecord lineage

LoRA fine-tuning

Activity responsibilities

Activity	Responsibility
fetch-base-model	Execute the fetch base model stage and publish its declared outputs for downstream activities.
prepare-examples	Execute the prepare examples stage and publish its declared outputs for downstream activities.
fine-tune-adapters	Execute the fine-tune adapters stage and publish its declared outputs for downstream activities.
evaluate	Execute the evaluate stage and publish its declared outputs for downstream activities.
publish-adapter	Execute the publish adapter stage and publish its declared outputs for downstream activities.
record-lineage	Execute the record lineage stage and publish its declared outputs for downstream activities.

Inputs

• Base-model revision
• Instruction dataset
• LoRA configuration

Outputs

• Adapter weights
• Evaluation report
• Published version

Provenance to preserve

• Model and dataset revisions
• GPU/runtime profile
• Evaluation results

Runnable example

This is the complete checked-in bundle for this pattern. Download the environment, scope, topology, workflow, input, container recipe, runner, and validation contract from this page before executing it.

Read setup instructions ↓Environment YAML ↓Execution scope YAML ↓Network topology YAML ↓Workflow YAML ↓Input data ↓Container recipe ↓Run the workflow ↓Validate outputs ↓Expected output manifest ↓

Verified local execution

These captures and the output manifest were produced by the fixture's local Docker run and validator. They are published with the same bundle as the runnable files.

Workflow evidence

Execution evidence

Output evidence

Open verified output manifest ↓

Execution considerations

Keep the base model near the accelerator or cache it as a reusable artifact. Every adapter must remain linked to its compatible base revision.

AkôFlow boundary: the engine schedules, deploys, executes, transfers data, and records evidence. The ML or agent framework remains an implementation choice inside each activity.