Prerequisites
- training mechanics
- evaluation and factory
Learning sequence
- Calibration and numeric formats
- Storage versus runtime kernels
- Pruning and physical topology
- Load and serve parity
- Model cards and public evidence
Hands-on lab
Audit one int4 or pruned artifact from source weights through file size, load parity, quality, RAM, and runtime measurement.
Mastery gate
Distinguish a smaller file, lower RAM, fewer physical operations, and faster wall-clock execution.
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/path-registry.json. The links below are the tracked evidence and explanatory sources preserved with the record.