Synthetic Data

Synthetic data is artificially generated data used to train or fine-tune AI models, created by algorithms rather than collected from real-world sources. Operators encounter it when they lack sufficient real examples for a task—e.g., generating 10,000 labeled chat logs via a larger model (like GPT-4) to fine-tune a smaller local model. The quality of synthetic data depends on the generating model's fidelity; low-quality synthetic data can introduce biases or artifacts that degrade the trained model's performance.

Synthetic data is produced through various methods: rule-based generation (e.g., templates for structured data), simulation (e.g., game engines for computer vision), or distillation from a larger model (e.g., using a 70B model to generate training pairs for a 7B model). In local AI, synthetic data is often used for fine-tuning when real data is scarce, expensive, or private. However, the generated data may lack the diversity or edge cases of real-world data, leading to model overfitting on synthetic patterns. Operators must validate synthetic data quality—e.g., by spot-checking outputs or measuring downstream task performance. Common pitfalls include repetitive phrasing, factual errors, or bias amplification from the teacher model.

An operator wants to fine-tune Llama 3.2 3B for customer support on a local RTX 4090. They have only 50 real chat logs. Using Ollama, they run ollama run llama3.1:70b to generate 5,000 synthetic Q&A pairs by prompting with "Generate a customer support conversation about [topic]." The resulting dataset is saved as JSONL and used with unsloth for LoRA fine-tuning. The operator then evaluates the fine-tuned model on 10 held-out real logs to check for quality degradation.

In a typical fine-tuning workflow, synthetic data generation is a preprocessing step. For example, using Hugging Face Transformers, an operator loads a teacher model (e.g., meta-llama/Llama-3.1-70B-Instruct) and runs inference on a set of seed prompts to produce training examples. The generated data is formatted into a dataset (e.g., datasets.Dataset) and used with SFTTrainer. Operators often filter synthetic data by removing low-confidence outputs (e.g., using the teacher model's log probabilities) or deduplicating near-identical responses.