Core ML Model Package
AI & Machine LearningMLPACKAGE packages Core ML models and associated data. ML Program separates program structure from weights and requires this package format. Extracting only a model file can omit weights; preserve the complete package.
- What it is used for
- Packaging Core ML models with weights/companions
- What the file contains
- Binary data
All sections, questions and sources
Content reviewed： · Editor：Kai
Opening steps
Choose your environment or purpose to see its steps.
Menu labels and support vary by app, OS and version. Preserve the original before making changes.
Troubleshooting by symptom
- Weights missing: recover the complete package instead of a partial copy.
- Older OS unsupported: check model type and deployment requirements.
- I/O mismatch: compare specification and preprocessing.
- Missing internal weights: check for a partial package copy or changed structure.
- predict unavailable on Linux: specification inspection and Core ML execution have different platform requirements.
- Image type error: compare image/array feature type, name, shape and color order.
MLPACKAGE → compatible persistence / reconvert original model
- Identify model type/external weights and preserve the complete original.
- For another format, use supported conversion from the original model and validate results/deployment target.
- Record deployment target, input types and precision; compare source, converted and device predictions.
ML Program cannot simply be saved/renamed to MLMODEL. Other model types such as neural networks have different persistence options.
Preserve the full package
ML Programs separate program and weights; transfer the package as one artifact and preserve its internal references.
Readable specifications do not ensure device execution; check Xcode/tool and OS requirements.
Source for these steps：Apple · Model Prediction — Core ML Tools / Apple · Convert Models to ML Programs
When predictions differ
Compare resizing, color order, normalization, names and labels against the source; record conversion precision.
Compute-unit selection depends on model and environment; verify output tolerances and actual device performance.
Source for these steps：Apple · Model Prediction — Core ML Tools / Apple · Convert Models to ML Programs
Related-format differences
| Format | Main role | What to check |
|---|---|---|
| MLMODEL | Single Core ML model | Model type/deployment target |
| MLPACKAGE | Model component package | Preserve internal structure |
| MLMODELC | Compiled Core ML artifact | Distinct from source model storage |
Handling and security
Use trusted models and legitimate tools; do not execute unfamiliar companion scripts. Avoid exposing training/business information through public converters and check model usage/redistribution rights.
Questions and answers
Can I copy only the internal model file?
Retain the package because external weights/companions may be required.
Can I rename it to MLMODEL?
ML Program requires packaging that includes external weights.
Does packaging make it runnable on every OS?
Check deployment and Core ML runtime requirements.
Is MLPACKAGE a ZIP file?
It packages model components; renaming it as ZIP is not a supported loading workflow.
Does Mac success imply every iPhone is supported?
Inspect model type, minimum OS and operator support and test the target device.
Format facts: name, steward and MIME type
- Formal name
- Core ML model package
- Steward / specification
- Apple
- MIME type
- Depends on the producer or server; an extension alone does not establish a MIME type.
Primary and official sources
- Official documentationModel Prediction — Core ML Tools
Documents model inspection and prediction environments.
Apple · Link checked： - Official documentationConvert Models to ML Programs
Explains ML Program weights and MLPACKAGE requirements.
Apple · Link checked：