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Practical guides to fine-tuning, distillation, and deploying small language models.
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 is 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 is the wrong choice.
What Is LoRA Rank?
LoRA rank is the inner dimension of the low-rank update matrices injected into a frozen model — the one knob that sets how much an adapter can learn.
What Is Quantization?
Quantization stores model weights at lower numerical precision so the same architecture occupies less memory and streams faster — how the block-scale mechanism works, what each bit width costs, and where the damage shows up first.
What Is Question Answering as a Training Task?
The question-answering task type trains a model to return a targeted answer rather than a summary — what distinguishes it from the two QA variants, what the job description must specify, and how the answers get scored.
What Is Text Classification with a Small Language Model?
Text classification trains a small model to emit exactly one label from a fixed set — how the task works on distil labs, what the training files contain, and the measured lift fine-tuning gives over a base model.