Artifact contract
A comparison of owned MLX weights, Core ML deployment, Apple Foundation Models, and browser runtimes grounded in measured capability limits.
This architecture decision record is classified as guided replay. Workload guidance: read-only evidence synthesis.
Build
Map one use case across capability, context, RAM, energy, ownership, and distribution constraints.
Modify
Add a new runtime only with a directly comparable evidence row.
Tune
Tune routing and deployment policy; do not hide a capability gap behind serving changes.
Prove
Use the same task gate plus latency, RAM, tok/s, energy, setup, and ownership constraints.
Package
Record the chosen runtime, rejected alternatives, evidence, and reversal conditions.
CLI surface
- Run: posttrainllm hf-load
- Run: posttrainllm ane-validate
- Run: posttrainllm coreml-serve
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.
- scripts/fm_agent_bridge.swift ↗
- native-mac/Sources/TinyGPT/CoreMLServe.swift ↗
- Map one use case across capability, context, RAM, energy, ownership, and distribution constraints. ↗
- Add a new runtime only with a directly comparable evidence row. ↗
- Tune routing and deployment policy; do not hide a capability gap behind serving changes. ↗
- Record the chosen runtime, rejected alternatives, evidence, and reversal conditions. ↗