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Practical guides to fine-tuning, distillation, and deploying small language models.
Can a Small Model Beat Its Teacher?
Yes, on narrow tasks — a 0.6B student beat a 120B teacher by 29 points and a 350M student beat the same teacher on two of three tool-calling benchmarks. The measured figures and why it happens.
Does Base Model Accuracy Predict Fine-Tuned Performance?
Across four mid-size models on seven tasks the base ranking inverted after fine-tuning — the worst zero-shot model tied for best once trained. Here are the ranks, the method, and what still transfers.
How Long Does Fine-Tuning Take?
Published distil labs runs land between roughly six and twelve hours end to end. Here is what those figures cover, which stage eats the clock, and what moves it.
How Many Examples Do You Actually Need to Fine-Tune a Model?
Twenty labelled examples is the documented floor and 20 to 100 the usual range — here are the published results behind those figures and what moves the number in either direction.
How Much Traffic Do You Need Before Traces Are Useful?
There is no published traffic threshold for trace-based training. This works out what the documented processing pipeline — dedup, seed splitting, relevance filtering, relabelling — implies about volume instead.
How Much VRAM Does a 1B, 3B or 8B Model Need?
Working forwards from a chosen model size to the card that can serve it, including the KV cache term computed from published attention configs — the part that decides whether a model that loads will still run at your context length.
LoRA vs QLoRA: Which Should You Use?
QLoRA is LoRA with the frozen base model quantised to 4 bits. Here is what that buys you, what it costs, and which one to enable for a small student model.
On-Device LLM Inference in 2026
Published throughput, footprint and accuracy figures for language models running on phones, embedded modules and commodity CPUs, each reported with the hardware and model it was measured on rather than as a general claim.
Open-Book vs Closed-Book QA
Two ways to make a small model answer questions about your documents: retrieve the passage at query time, or bake the knowledge into the weights. The data, the operations, and the provenance differ sharply.