Refer to the ZML GitHub documentation for specific API commands to load .zip bundles into your Zig project. :
:If you are a developer using the framework, you typically load this bundle using the ZML runtime. This allows the system to convert the model into a physical address on an accelerator (like a GPU or NPU) for execution.
In this context, a "bundle" is a portable package that allows you to distribute machine learning models along with their necessary metadata and weights. Quick Guide to Handling ZML_BUNDLE.zip
: JSON or specialized files describing the model's inputs, outputs, and requirements.
: Often represented in MLIR or Zig-specific formats.
:Since it is a standard ZIP file, you can extract it using common tools like 7-Zip or WinZip . On most modern operating systems, you can simply right-click the file and select "Extract All" . Contents to Expect :Inside the bundle, you will likely find:
: Storing specific iterations of a model as a single, immutable file.
: Binary files containing the trained parameters of the model.
Refer to the ZML GitHub documentation for specific API commands to load .zip bundles into your Zig project. :
:If you are a developer using the framework, you typically load this bundle using the ZML runtime. This allows the system to convert the model into a physical address on an accelerator (like a GPU or NPU) for execution.
In this context, a "bundle" is a portable package that allows you to distribute machine learning models along with their necessary metadata and weights. Quick Guide to Handling ZML_BUNDLE.zip ZML_BUNDLE.zip
: JSON or specialized files describing the model's inputs, outputs, and requirements.
: Often represented in MLIR or Zig-specific formats. Refer to the ZML GitHub documentation for specific
:Since it is a standard ZIP file, you can extract it using common tools like 7-Zip or WinZip . On most modern operating systems, you can simply right-click the file and select "Extract All" . Contents to Expect :Inside the bundle, you will likely find:
: Storing specific iterations of a model as a single, immutable file. In this context, a "bundle" is a portable
: Binary files containing the trained parameters of the model.