NanoChat Task Context

The task uses one fixed local base model: NanoChat.

Useful files in a generated task directory:

• model.py defines the only allowed architecture.
• prepare.py creates the fixed train/eval data and base checkpoint.
• train.py is a starter nanochat-style masked SFT baseline with optional reward training. You may replace or extend it.
• evaluate.py is the fixed benchmark evaluator. It scores generative tasks by task-specific answer extraction and scores multiple-choice tasks from next-token logits over the available letters. Do not modify it.
• timer.sh prints the remaining task budget.

The best model must be saved as final_model/config.json and final_model/model.pt. The evaluator rejects architecture substitutions.