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Practical guides to fine-tuning, distillation, and deploying small language models.
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, with 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.
How to Distill a Large Language Model into a Small One
A practical guide to distilling large language models into small, deployable models. Learn the end-to-end process, from choosing a teacher to deploying a student that matches its accuracy.
How to Fine-Tune a Small Language Model (Step-by-Step Guide)
Learn how to fine-tune a small language model for your specific use case. This step-by-step guide covers data preparation, training configuration, LoRA adapters, and deployment.
Is Fine-Tuning Worth It? When to Fine-Tune vs Prompt
Prompt engineering is fast and flexible, but fine-tuning delivers higher accuracy, lower latency, and lower cost at scale. Learn when each approach makes sense and how to decide.