Buildable artifact · stage 7

Validated MLX specialist package

A model or adapter exported with identity, tensors, tokenizer, prompt contract, and evidence that the consumer can load it correctly.

Readiness
runnable lab
Kind
deployable model bundle
Workload
conversion is bounded; validation loads the selected model
Stage
Make it smaller and portable

Artifact contract

A model or adapter exported with identity, tensors, tokenizer, prompt contract, and evidence that the consumer can load it correctly.

This deployable model bundle is classified as runnable lab. Workload guidance: conversion is bounded; validation loads the selected model.

Build

Export a .tinygpt model, adapter, or Hugging Face directory into the MLX layout.

Modify

Change the packaging boundary or consumer metadata without changing model claims.

Tune

Choose quantization or pruning only against frozen quality, RAM, size, and speed gates.

Prove

Verify round-trip load parity, file size, peak RAM, tok/s, and target quality.

Package

Publish the bundle with model card, hashes, environment, limits, and decision.

CLI surface

  • Run: posttrainllm export-mlx
  • Run: posttrainllm validate
  • Run: posttrainllm experimental gptq
  • Run: posttrainllm experimental prune-structured

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.