Post-training in Nebius Token Factory lets you adapt foundation models to your data and tasks. By training on your own examples, the model learns your domain patterns directly improving accuracy, stability, and reducing prompt complexity.Supervised fine-tuning allows you to train on large datasets without prompt-length limits. This gives you tighter control over model behavior, reduces the need for manual prompt engineering, and can lead to lower inference cost and latency.Custom speculator training allows you to train a drafter tuned to your own base model and workload, rather than relying on a generic speculator. This gives you higher inference throughput and lower latency during speculative decoding, driven by better token acceptance rates, while preserving the base model’s output quality.
