> ## Documentation Index > Fetch the complete documentation index at: https://docs.tokenfactory.nebius.com/llms.txt > Use this file to discover all available pages before exploring further. # Overview > Post-training on Nebius Token Factory 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**](https://docs.tokenfactory.nebius.com/post-training/how-to-fine-tune) 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**](https://docs.tokenfactory.nebius.com/post-training/custom-speculator) 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.\ \ Upcoming Advanced post-training options including: * **Reinforcement Fine-tuning** (limited professional service [request access](https://nebius.com/reinforcement-fine-tuning-research-program)) You can currently run **long-context supervised fine-tuning** on full model weights or **LoRA adapters**. If you're ready to fine-tune or want to explore the workflows involved, start here: Step-by-step guide to preparing data, launching a job, and evaluating results. Supported base models for training and inference. Create and manage datasets for training and validation. ***