5 tools
Fine-Tuning Dataset Tools
Prepare, validate and split data before you pay to train on it.
A fine-tuning upload rejects the entire file for one malformed row, and reports a line number on a file with fifty thousand of them. Validating locally first turns that into a specific line and a specific message.
The subtler problem is duplication. An example appearing forty times teaches the model that its pattern is forty times more important, and a duplicate that lands in both your training and validation sets makes your evaluation report memorisation as accuracy.
- JSONL Validator for Fine-Tuning DatasetsCatch the broken line before the upload rejects your whole file.
- Dataset Deduplicator for Fine-TuningFind the near-duplicates that skew training and waste budget.
- Train and Validation Set SplitterA reproducible split, so today’s evaluation is comparable to tomorrow’s.
- Chat Messages Array BuilderWrite a conversation as plain lines, get a valid messages array.
- OpenAI to Anthropic Format ConverterMove a request between the two message formats without losing the system prompt.
Which one you need
Validate before you upload. A batch or training job rejected after an hour of queueing is an hour you do not get back, and the causes are almost always mechanical: a pretty-printed object spanning lines, a trailing blank line, a wrapping array left over from a JSON export.
Deduplicate next. Near-identical examples teach the model that the duplicated pattern is more important than it is, and they inflate the token count you pay to train on.
Split with a holdout you will not look at. A fine-tune evaluated on data it saw is a fine-tune with no measurement attached.
Before any of it, ask whether fine-tuning is the right instrument. It teaches format and behaviour; it does not supply facts that change. Knowledge that would be different next month belongs in retrieval.
Questions
How many examples does fine-tuning need?
Providers commonly state a minimum in the low hundreds, but quality dominates quantity. A few hundred carefully checked examples outperform thousands of inconsistent ones, because the model learns the inconsistency along with everything else.
Should I fine-tune or improve the prompt?
Prompt first, almost always. Fine-tuning earns its cost when you need consistent format or behaviour at high volume and the prompt achieving it has grown long. Fine-tuning to fix an ambiguity is expensive and does not fix the ambiguity.