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Practical guides to fine-tuning, distillation, and deploying small language models.

Train an SLM for Voice Assistant Command Routing

In voice, the model size is chosen by the latency budget before anything else — how to spend a 500–800ms turn, what the SLM must and must not emit, and how to train it on multi-turn tool calling.

Turn Production Traces Into Training Data

A step-by-step recipe for converting logs from a live LLM feature into a training and test set, using distil traces upload and distil upload create-from-traces.

What Size Model Do You Need?

A constraint-first way to choose a student model size, with the parameter tier each constraint implies, the evidence that bigger does not reliably win, and the four sizing mistakes that cost the most time.

Which Task Type Should You Pick?

A row-by-row decision guide for choosing between the six distil labs task types, the tie-breaks when two of them fit, and the four mistakes that cost a training run.

Writing a Job Description for Synthetic Data Generation

How to write the job_description.json that defines correct behaviour for your task — the field that carries the normative signal for every generated training example.

Few-Shot Fine-Tuning: Train a Model with 10 Examples

Learn how few-shot fine-tuning lets you train a small language model with as few as 10 labeled examples — and when it outperforms in-context learning.

How to Fine-Tune an LLM Without a GPU

You don't need expensive hardware to fine-tune a language model. Learn how cloud-based distillation platforms let you train custom SLMs from a prompt — no GPU required.

Fine-Tune with Synthetic Data: Generate Training Data from a Prompt

Learn how to use synthetic data generation to create high-quality training datasets for fine-tuning small language models — even when you have little or no labeled data.

Generate Synthetic Training Data for LLM Fine-Tuning

Learn how to generate high-quality synthetic training data using a teacher LLM to fine-tune smaller, faster models — even when you have little or no labeled data to start with.