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.
📖 Read the full source: r/LocalLLaMA
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