Prepare, format, and validate datasets for supervised fine-tuning and preference training. Use when converting raw data into training format, applying chat templates, configuring sequence packing, generating synthetic training data, or writing a dataset card before a run.

Dataset Curation This skill assumes finetuning method selection already routed here — the next step is preparing data, not choosing a method. What follows: format selection by target method, the template/packing mechanics behind the most common silent training failures, rules for mixing in synthetic data without collapse, and the dataset card that closes out Phase 2 before a run starts. Input: raw examples (demonstrations, preference judgments, or task prompts) plus a routing decision from finetuning method selection . Output format: a formatted, packed, validated JSONL dataset plus a completed dataset card — the Phase 2 artifact /finetune checks before launching training. Format Selection Method Shape Rows SFT, single turn Instruct ( instruction / response or prompt / completion ) ~1,000+ floor SFT, multi turn Conversation / ChatML messages list ~1,000+ floor DPO / ORPO Preference pair ( prompt , chosen , rejected ) Method dependent, see preference optimization KTO Unpaired ( prompt , completion , label ) Method dependent, see preference optimization GRPO / RLVR Prompt only ( prompt + verifier metadata) Method dependent, see grpo rlvr training ~1,000+ rows is the recommended floor for SFT , not a target. Below it, a handful of low quality or duplicate examples can dominate the gradient; above it, quality over quantity — a smaller verified, deduplicated set beats a larger noisy one. The ChatML shape, for orientation; the other four formats plus a ShareGPT conversion note live in references/formats and templates.md : Chat Templates and Loss Masking Apply the target model's chat template before any concatenation or packing, never after — packing raw text and templating the packed blob afterward corrupts turn boundaries, landing role markers in the wrong place relative to each example. Train on assistant responses only. Mask the loss ( 100 in the labels tensor) over system/user turns and the template's own role markers — only assistant turn content tokens contribute to loss. Template/tokenizer mismatches are a top silent failure mode. A model trained against one chat template but served or evaluated with a different one degrades without erroring. Verify the same template string used in training is applied at inference and eval time. Keep the dataset in messages shape and let the trainer template and mask it ( assistant only loss=True in current TRL) — pre rendering to a flat text field destroys the turn boundaries masking needs. Full code sketch: references/formats and templates.md . Sanity check before training — decode only unmasked positions; expect only assistant text: Packing Without packing, 40–70% of compute is spent on padding — variable length examples batched at a fixed sequence length waste the gap between each example's length and the batch's max. Packing concatenates multiple examples into one sequence up to the max length, cutting most of that waste. Packing changes batch semantics. A packed sequence can contain several original examples, so "steps per epoch" and any LR schedule keyed to example count shift once packing is on — recompute schedule milestones against packed sequence count. MANDATORY: decode and manually inspect 5–10 packed sequences before scaling to a full run. Confirm example boundaries land where expected, template markers are intact per sub example, and the loss mask is still assistant only within each packed sequence. Not optional — packing bugs are silent (the loss curve looks normal) and only surface in eval quality, hours later: Synthetic Data Rules Keep ≥25% real data as a collapse guard. Training on a growing share of model generated data without a real data floor drives measurable quality collapse over successive generations — 25% real is the minimum that holds the line. General domain replay rows count toward this floor — "real" means "not generated for this task from this student," not "human authored." An all synthetic by construction dataset can meet the ≥25% floor through replay alone (see references/synthetic data.md 's Replay Mix Construction recipe); state which rows count as "real" in the dataset card rather than leaving the floor structurally unmeetable. Magpie and rejection sampling are the workhorses. Magpie extracts prompts from the model's own template prior; rejection sampling generates several candidates per prompt and keeps only the ones a filter passes. Both beat naive single shot generation. Targeted, student aware generation beats static generation by 1.3–2x sample efficiency — aiming at the student's actual failure modes hits a quality bar with fewer filtered examples. Typical accept rates after filtering run 10–30%. Plan volume accordingly — a 10,000 row target at 15% accept needs ~65,000+ raw generations. Generation method ranking, filter funnel, replay mix construction, and distillation pattern: references/synthetic data.md . The Dataset Card Every dataset that reaches training gets a card — the required Phase 2 artifact /finetune checks before launching. The card is not free form documentation; it MUST carry these fields: Provenance — where every row came from (real source(s), synthetic method(s), or both), traceable to trace to training data output. Counts — total rows, and rows per split (train/eval/held out) if split. Synthetic/real ratio — the measured ratio, checked against the ≥25% real floor above. Dedup method — exact match, semantic (embedding threshold), or both; see the filter funnel in references/synthetic data.md . Template used — the exact chat template string/identifier, kept consistent through inference and eval — this is what ties an eval harness first run back to the checkpoint. Packing config — whether packing was used, max sequence length, and confirmation the 5–10 sequence manual inspection above was done. A dataset missing any of these six fields isn't ready for /finetune — the card is a gate, not a summary written after the fact. Phase 2 Exit Checklist Before handing off to /finetune , confirm: 1. Format matches the method (table above). 2. Template applied before concatenation. 3. Loss masked to assistant turns only. 4. 5–10 packed sequences decoded and read. 5. ≥25% real data in the final mix. 6. Dataset card complete — all six fields. References references/formats and templates.md — JSONL examples per format, current TRL masking code, and the ShareGPT conversion note. references/synthetic data.md — generation method ranking, filter funnel, replay mix construction, and teacher→student distillation pattern. Related skills: finetuning method selection routes here; lora qlora recipes , vision sft , and preference optimization consume the datasets this skill produces; trace to training data is the provenance source for graded trajectory datasets; eval harness first grades the resulting checkpoint.

dataset-curation