How-to

How to write unit tests with a local AI model

Last updated 2026-06-18 · Outlier v1.11.469

What you need first for “how to write unit tests with a local ai model”

Apple Silicon Mac, macOS 12 or later, the unified-memory minimum that the chosen tier requires (6 GB for Nano, 12 GB for Lite, 24 GB for Core / Code / Vision, 32 GB for Plus). Internet is required only for the one-time model download.

Steps

1. Open the function under test. Drag the file onto the chat window or use the project chip.
2. Ask for one focused test at a time. Local tier quality holds well for one test; quality on long batches drops.
3. Iterate inline. Run the suggested test in your terminal; paste failures back.
4. Pin the tier to Lite or Core. Quick is faster but its HumanEval pass@1 is much lower; not the right choice for code-shaped tasks.

What is the specific thing to know about this guide?

For test scaffolding the Lite tier is usually the right speed-quality balance on a 16 GB Mac, with Core reserved for trickier or long-context work.

What can go wrong with this guide?

• Lite’s code quality is good, not great; tricky tests sometimes need a manual second pass.
• Long batches of tests in one prompt drop quality; break them up.

How does this guide differ from the cloud equivalent?

Hosted test-writing tools log your function-under-test alongside the prompt. Outlier’s local path leaves no log on a third-party server.

For test scaffolding the Lite tier is usually the right speed-quality balance on a 16 GB Mac, with Core reserved for trickier or long-context work.

What does this guide not claim about “how to write unit tests with a local ai model”?

For test scaffolding the Lite tier is usually the right speed-quality balance on a 16 GB Mac, with Core reserved for trickier or long-context work.

This guide does not claim feature parity with cloud-side workflows for “how to write unit tests with a local ai model”. Specifically, the product surface in v1.11.469 covers chat, file attachment, the local agent loop, project scoping, and the model picker. Cross-device sync, team workspaces, and shared session history are out of scope and are not on the v1.9 backlog either.

What is the right tier for test writing?

Lite at 5 GB, 12 GB RAM minimum, 53.4 tok/s on M1 Ultra reference. That is the baseline. If the function under test is short and the test framework is well-known (pytest, vitest, jest), Lite handles it. For tricky fixtures or property-based testing, Core (24 GB RAM) is the upgrade.

Where does this guide fit in the rest of the lineup?

Lite is good enough for most pytest scaffolds; Core is the upgrade for tricky fixtures or property-based tests. Quick is not the right tier for any test work despite its fast tok/s.

One unique number

4 steps, zero network requests after the model is downloaded. Recommended pattern: one test per turn, paste the failure into the next turn, iterate until green.

Download Outlier for Mac

Requires Apple Silicon (M1, M2, M3, or M4) — Intel Macs are not supported. macOS 12+.