This repository implements the Neural Thickets approach for post-training LLMs, using black-box optimization and neuroevolution to discover diverse specialized models around pretrained weights. It leverages LoRA for efficient fine-tuning and provides tooling to post-train on custom or standard datasets. The top-performing specialized models are then distilled into a unified model.