Preserve the meaning of an input
A converted model can accept a tensor while receiving the wrong representation of the original data. Before exporting, write down dimensions, channel order, numeric range and the transformation from an application input to that tensor. For text, keep the tokenizer and formatting rules with the model. For images, retain the resize and padding policy. These details belong to the deployment artifact because the exported graph may not contain every operation performed by the original application.
Keep a comparison set
Select a small collection of inputs that exercise the task’s awkward cases as well as ordinary examples. Save the reference outputs before conversion, along with a clear rule for acceptable differences. A classification label alone can conceal a large score change, while an exact numerical match may be unnecessary for another application. Define the comparison around what the product needs to preserve. The resulting record gives a conversion failure somewhere specific to begin, instead of inviting repeated blind exports.
Separate export from quantization
ONNX Runtime’s documentation distinguishes quantization approaches and describes tools for comparing activations and weights when accuracy drops. Keep an unquantized exported baseline before adding that extra transformation. If the first converted model already disagrees with the reference, lowering precision makes the investigation harder to interpret. Establish correctness through each boundary, then assess the quantized version using the same inputs. Record the tool version and its settings so the process can be repeated when the model changes.
Deliver a complete bundle
Package the graph, any external weight files, preprocessing configuration, label mapping and comparison inputs as one identified release. Include the target runtime and the command that validates the bundle on the edge device. A passing export command establishes that a file was produced; it does not establish that the application interprets it correctly. Accept the release when another developer can reconstruct an input, execute it and understand how the resulting output was judged.
Sources & publication notes
Retrospective period: April 2025. Written and published November 2, 2026. Product announcements establish release timing; support pages are linked as current references.