Model Distiller Workflow

Why Model Distiller Workflow Is Worth Using

Automatically iterate over a dataset and use a teacher LLM to generate high-quality fine-tuning pairs for your small models. Free. This page is built for people who want a fast path to a working result, not a vague prompt-and-pray workflow. If you need a more reliable first draft, cleaner output, or a repeatable workflow you can hand to a teammate, Model Distiller Workflow is designed to shorten that path.

Most visitors use Model Distiller Workflow because they need something specific done now: a deliverable, a decision, or a workflow checkpoint. The sections below show the fastest way to get value from the tool and the adjacent pages that help you keep going.

How to Use Model Distiller Workflow

Automate the creation of synthetic instruction sets.

1. 1Upload a batch of raw input data
2. 2Configure the Teacher LLM's system instructions
3. 3Run the generation pipeline in the background
4. 4Export a clean JSONL file ready for LoRA fine-tuning

Who Is Model Distiller Workflow For?

Machine learning practitioners exploring synthetic data.

What a Good Result Looks Like

A strong outcome from Model Distiller Workflow is not just “some output.” It should be usable with minimal cleanup, aligned to the task you opened the page for, and specific enough that you can paste it into the next step of your workflow without rewriting everything from scratch.

If the first pass feels too generic, use the use cases, FAQs, and related pages here to tighten the scope. That usually produces better results faster than starting over in a blank chat.

Frequently Asked Questions