Program

We want to post-train the local NanoChat base model to maximize held-out eval_score.

The fixed benchmark files are:

• prepare.py
• evaluate.py
• model.py

The starter experiment file is:

• train.py

Start with:

uv run prepare.py

Then improve the post-training method in train.py, or create additional supporting files. Prefer nanochat-style methods: chat task mixtures, masked assistant-token SFT, task-specific eval loops, rejection sampling, curriculum SFT, distillation, or lightweight reward training over generated completions. You may change data mixing, schedules, optimization, loss shaping, or other training strategy details, but keep the benchmark honest:

• do not train on the eval split
• do not modify evaluate.py
• do not modify generated eval records
• do not substitute another architecture
• save the best checkpoint to final_model/

Always finish with:

uv run evaluate.py --model-path final_model

For managed cloud runs, use the post-training HF Jobs launcher:

uv run scripts/hf_job.py launch --mode experiment
uv run scripts/hf_job.py logs <JOB_ID> --follow --output /tmp/posttrain-run.log

The job persists final_model/, metrics, logs, and the source snapshot under runs/<JOB_ID>/ in the mounted POSTTRAIN_HF_BUCKET.

For Pi Agent orchestration, start Pi in this project root and run:

/posttrain "nanochat sft improvements" 1 3

The Pi flow is project-local and should not use the pre-training /autolab workflow.

To mirror the PostTrainBench workflow more closely, create a sandbox first:

uv run scripts/create_task.py --num-hours 1

Read the generated prompt.txt, work inside that directory, and finish by running its copied evaluate.py.