Skillware adds synthetic data generator with entropy scoring for local model fine-tuning

Skillware has added a new Synthetic Data Generator skill to its library, designed specifically for fine-tuning local models while addressing the problem of generic synthetic data leading to model collapse.

Key Features

The tool includes several specific capabilities:

• Entropy Scoring: Uses a zlib compression-ratio heuristic to mathematically score how diverse the output is before saving it. This helps identify and filter low-entropy data that could contribute to model collapse.
• Local-Ready: Works out-of-the-box with Ollama for local model integration. Also supports Gemini and Anthropic models for generating high-reasoning batches when needed.
• Structured Output: Generates perfect JSON batches formatted specifically for .jsonl fine-tuning pipelines, making it ready for immediate use in training workflows.

Problem Addressed

The tool specifically targets the issue where generic synthetic data causes models to "parrot themselves" during fine-tuning, a phenomenon known as model collapse. By scoring output diversity before saving, it helps ensure training data maintains sufficient variation.

The source indicates this is a new addition to the Skillware library, available for developers working with local models who need better synthetic data generation for fine-tuning tasks.