> Canonical page: https://posttrainllm.com/learn/artifacts/tiny-overfit-receipt

Buildable artifact · stage 2

# Tiny-overfit correctness receipt

A minimal proof that data loading, forward pass, backpropagation, optimizer state, sampling, and checkpoint reload agree.

**Readiness**
 runnable lab

**Kind**
 training evidence

**Workload**
 bounded correctness gate

**Stage**
 Make training trustworthy

## Artifact contract

A minimal proof that data loading, forward pass, backpropagation, optimizer state, sampling, and checkpoint reload agree.

This training evidence is classified as runnable lab. Workload guidance: bounded correctness gate.

## Build

Run the smallest overfit gate before any ordinary training run.

## Modify

Change only one of corpus, seed, precision, or optimizer settings.

## Tune

Use learning-rate and clipping changes only after gradients and expected initial loss are verified.

## Prove

Require memorization, checkpoint round-trip, and a sensible held-out comparison.

## Package

Retain the config, seed, loss trace, sample, and validated checkpoint as one receipt.

## CLI surface

- Run: posttrainllm train
- Run: posttrainllm eval
- Run: posttrainllm debug-loss
- Run: posttrainllm validate

## How to read this dossier

This canonical page separates the retained repository decision from the material that informed it. A source may describe an external claim, historical measurement, local observation, or planned procedure; those evidence classes are not interchangeable. Follow the provenance links before reusing a number or method.

Status and disposition describe what PostTrainLLM retained when this record was indexed. They are not a live product promise, a newly run benchmark, or permission to restart historical work. Unknown or unmeasured fields remain unknown rather than being treated as zero.

To reuse the record, first name the exact claim you need and trace it to the linked source. Then check whether the original environment, model revision, data split, evaluator, hardware, and budget match the proposed use. If they do not, treat the record as a hypothesis or design reference and run the smallest fresh comparison that can falsify it. Preserve negative outcomes and regressions beside any improvement; a local win on one slice does not silently become a general capability claim.

## Source provenance

The normalized record comes from docs/learn/artifact-journey.json . The links below are the tracked evidence and explanatory sources preserved with the record.

- [python_ref/evaluate.py ↗](https://github.com/PostTrainLLM/posttrainllm/blob/main/python_ref/evaluate.py)
- [python_ref/checkpoint.py ↗](https://github.com/PostTrainLLM/posttrainllm/blob/main/python_ref/checkpoint.py)
- [Run the smallest overfit gate before any ordinary training run. ↗](https://github.com/PostTrainLLM/posttrainllm/blob/main/tests/README.md)
- [Change only one of corpus, seed, precision, or optimizer settings. ↗](https://github.com/PostTrainLLM/posttrainllm/blob/main/configs/training.json)
- [Use learning-rate and clipping changes only after gradients and expected initial loss are verified. ↗](https://posttrainllm.com/docs/techniques/galore_and_stability/)
- [Require memorization, checkpoint round-trip, and a sensible held-out comparison. ↗](https://posttrainllm.com/docs/guides/training_guide/)
- [Retain the config, seed, loss trace, sample, and validated checkpoint as one receipt. ↗](https://posttrainllm.com/docs/factory/run-schema/)

[Read the Markdown equivalent →](https://posttrainllm.com/learn/artifacts/tiny-overfit-receipt.md)

Related evidence

- learning path [Make training trustworthy](https://posttrainllm.com/learn/paths/training-mechanics)
- recipe [GaLore and training stability](https://posttrainllm.com/recipes/galore-stability)
- recipe [Optimizer and schedule selection](https://posttrainllm.com/recipes/optimizers)

 Canonical record learning-artifact:tiny-overfit-receipt