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Practical guides to fine-tuning, distillation, and deploying small language models.
Three Ways to Get Training Data for a Small Language Model
A decision guide to the three routes for getting training data onto the distil labs platform: live traffic collection, uploading existing traces, or a minimal hand-written dataset.
Train an SLM for Content Moderation
A moderation model inherits whatever your policy leaves undecided: how to convert policy into labels, seed from overturned appeals, and keep the model inside a review path it can't bypass.
Train an SLM for Document QA over Contracts
Contract QA lives or dies on chunking: a clause split across two chunks produces a confidently wrong answer. How to segment agreements, build open-book training data, and grade the result.
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 upload create-from-traces.