Self-distilled fine-tuning toolkit lets developers train custom models without proprietary backends. Open-source project Self-Distilled enables lightweight fine-tuning on local hardware by distilling weights from open models like Llama 2. Critical for developers avoiding cloud dependency or cost, with early benchmarks showing 80% accuracy retention on GLUE tasks.
Opening Kapyn…