run-hugging-face-training-smokelisted

# Run Hugging Face Training Smoke Run the smallest experiment that can disprove the training setup. Treat a passing smoke as permission to consider a longer run, never as model or product acceptance. ## Establish the boundary 1. Record the hypothesis, maximum optimizer steps, hardware, timeout, budget, seed, and stop gates. 2. Freeze dataset splits, model revision, tokenizer revision, prompt contract, held-out manifest, and validator revision. Hash local inputs before renting compute. 3. Confirm the user has authorized paid compute and any artifact upload. Keep repositories private unless explicitly told otherwise. 4. Load credentials from the authorized environment without printing or copying them into commands, logs, manifests, or source. ## Preflight locally Run `scripts/preflight_runner.py` against the exact submitted runner: ```sh python scripts/preflight_runner.py path/to/runner.py \ --require "load_best_model_at_end" \ --forbid "print(os.environ" ``` Then execute the runner's cheapest supported dry run or stdin simulation. Verify: - imports and argument parsing succeed; - dataset and model revisions resolve; - chat/template formatting produces completion-only labels as intended; - the output directory and private Hub target are explicit; - evaluation occurs at the intended cadence; - the metric used for best-model selection matches the emitted metric; - held-out evaluation explicitly reloads the recorded best checkpoint; - incremental logs and results survi