Use case
LoRA adapters, supervised fine-tuning, knowledge distillation, RLHF, and quantization, from raw data to deployment-ready weights.
Fine-tuning and adaptation work is repetitive: preparing data, picking hyperparameters, running training, and validating weights, all while hardware and cost constraints keep shifting.
NEO drives the loop end to end: curated datasets, adapter and full fine-tune runs, distillation and quantization checks, and reproducible artifacts you can promote to staging without babysitting notebooks.
