Chat templates¶
Chat templates turn structured messages into the exact prompt text expected by the selected tokenizer. AReno keeps this rendering close to rollout and serving so a training run and an OpenAI-compatible endpoint can use the same model conversation format.
Use chat templates when a model expects role-based messages such as system,
user, assistant, or tool-related turns. Dataset loaders should keep raw
task fields and normalized prompts separate; tokenizer-specific formatting
belongs in the training or serving path.
Thinking mode¶
Some reasoning or chat checkpoints expose an enable_thinking option through
their tokenizer chat template. AReno’s CLI exposes --disable-thinking for
training and serving, which passes enable_thinking=False when supported and
falls back to the normal template call otherwise.
Where to go next¶
Training CLI reference documents the training flag.
Inference CLI reference documents the serving flag.
Dataset formats explains how dataset rows provide prompt inputs.