Your Fine-Tune Has No Data Yet. Here's How to Build the Dataset Without Annotators.

TL;DR: Generate a JSONL fine-tuning dataset without annotators — describe your task, seed it with examples, HuggingFace's Synthetic Data Generator drafts and LLM-judges them, then push straight to the Hub.

What you'll be able to do after this: Generate a chat-format fine-tuning dataset from a task description and five seed examples, filter it with an LLM judge, push the result to HuggingFace Hub, and load it into TRL's SFTTrainer in a single datasets.load_dataset() call.

Three key ideas:

• LLM as teacher. A frontier model (default: Llama 3.3 70B via the free HuggingFace Inference API) generates diverse variations of your seed examples. The seed shapes the style; the LLM fills the volume.
• LLM as judge. A second pass scores each generated example for quality and task alignment. Low-scoring examples are filtered before the dataset is pushed. The 2026 finding from synthetic-data research: an unfiltered dataset is reliably worse than a smaller filtered one.
• JSONL chat format. Each entry is {"messages": [{"role": "system", ...}, {"role": "user", ...}, {"role": "assistant", ...}]} — the same format TRL's SFTTrainer and Unsloth's FastLanguageModel both consume directly.

Walk-through:

1. Open the Synthetic Data Generator Space — free, no GPU required.
2. Describe your dataset in plain language: "customer support chat for a Python SDK, questions about rate limits and auth errors."
3. Add 3–5 seed examples to anchor the style and vocabulary.
4. Select Chat Dataset, set sample count (200 is enough to verify quality), click Generate.
5. Review a sample in the Argilla panel; re-generate any section that's off.
6. Push to Hub → datasets.load_dataset("your-org/dataset-name") in your training script.

Limits: The Spaces version only handles text classification and chat datasets; RAG, preference, and eval tasks require the distilabel Python SDK directly. Generation speed is ~20 samples/minute on the free Inference API — budget an hour for 1,000 examples. Quality is bounded by your seed: vague seeds produce coherent but useless data. Switch to a local or paid inference endpoint for larger volumes.

Sources: Introducing the Synthetic Data Generator — HuggingFace Blog · Synthetic Data for LLM Fine-Tuning in 2026 — Future AGI · distilabel docs — Argilla/HuggingFace