Learn
Practical guides to fine-tuning, distillation, and deploying small language models.
Train an SLM for Intent Detection
Intent detection is decided at the boundaries between intents, not in the middle of them: how to derive intents from backend actions, mine ambiguous utterances, and handle out-of-scope input.
Train an SLM for PII Redaction
Redaction has asymmetric costs: a missed identifier is a disclosure, a spurious one is noise. How to write the policy, seed the obfuscated cases, and grade a model that has to be exhaustive.
Train an SLM for Support Ticket Triage
Ticket triage fails on taxonomy design, not on model capacity: how to write class descriptions your annotators agree on, seed the confusable pairs, and train a router with the distil CLI.
Train an SLM for Text-to-SQL
Text-to-SQL fails on schema grounding rather than on SQL syntax: how to feed the schema, seed across the difficulty range, and evaluate queries that are correct without matching the reference string.
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 seed-dataset 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 doesn't reliably win, and the sizing mistakes that cost the most time.
When Does Self-Hosting Beat an API?
Five criteria that decide whether a dedicated GPU is cheaper than per-token billing (utilisation, task shape, latency budget, data residency, and team capacity), with the threshold for each.
When Not to Use a Small Language Model
Five conditions that should make you walk away: an undefined task, weekly-changing requirements, low volume, a need for broad capability, and a teacher that can't solve it either.