Post training - Supervised Finetuning
In previous posts, I explored how to pretrain a language model. The setup for pretraining used Llama 3.2 1B base model trained on tokyotech-llm/swallow-code-v2 dataset for Fill-in-Middle (FIM) task. The mistake here was using narrow code-only corpus as pretraining dataset. A more practical pipeline would be to begin with a broadly pretrained base model, optionally continue pretraining it on high-quality code, and then apply SFT using verified prompt–response demonstrations. Pretraining task are used to learn language representation across various tasks. ...