| This is very analogous to tokenization - you generally get the | |
| best performance for inference or fine-tuning when you precisely match the tokenization used during training. | |
| If you're training a model from scratch, or fine-tuning a base language model for chat, on the other hand, | |
| you have a lot of freedom to choose an appropriate template! LLMs are smart enough to learn to handle lots of different | |
| input formats. |