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Practical guides to fine-tuning, distillation, and deploying small language models.
Qwen3.5 2B Fine-Tuning Guide
What changed between Qwen3 and Qwen3.5 at the 2B tier, what a 262K context window is and isn't good for, and why no published distil labs benchmark covers this model yet.
The Six Task Types for Training a Small Language Model
The six task types distil labs supports (classification, question answering, open-book QA, closed-book QA, tool calling, multi-turn tool calling) and what each one changes about your data, your metric, and your model choice.
SmolLM2 135M: The Smallest Useful Student
At 135M parameters SmolLM2 is the floor of the distil labs catalog: what a model this size can still learn, the three hard constraints that come with it, and when picking it is the right call.
The 10x Inference Tax
The inference tax is the premium you pay for running a general-purpose frontier model on a task a specialised small model handles equally well. Here's where the multiple comes from.
Train a Model from 1% of Production Traffic
The lowest-effort route to a task-specific small model: let a small share of your live traffic be collected as training material, with nothing for you to write or label.
What Is a Small Language Model?
A small language model is a language model small enough to fine-tune and serve on commodity hardware, roughly 100M to 10B parameters, and the size band where task-specific training pays off most.
What Is a Student Model in Knowledge Distillation?
The student model is the small model you fine-tune on teacher-generated data and actually deploy. Sizes, how it learns, and what it gives up compared with the teacher.
What Is a Teacher Model in Knowledge Distillation?
A teacher model is the large LLM that generates and validates synthetic training data for a smaller student. Here's what it does, which models qualify, and why you never deploy it.
What Is GGUF?
GGUF is the single-file binary format that packages a model's tensors and its metadata together for llama.cpp-family runtimes: what it stores, what its quantization type names mean in bits per weight, and when it's the wrong choice.