BUNDLE_README.md

CelsiusToKelvin Edge Bundle

MatrixAI model exported for edge/production inference. Actions remain simulate_only. This bundle only provides predictions.

Quick start

This model is self-usable: feed raw, human-readable values and get back a single numeric value. Normalization and category encoding are handled for you by predict.py.

python -m venv .venv
. .venv/bin/activate            # Windows: .venv\Scripts\activate
pip install -r requirements.txt
python predict.py --input example_input.json

That should reproduce expected_output.json. From your own code:

from predict import MatrixAIModel

model = MatrixAIModel()                 # loads inference_spec.json next to predict.py
print(model.predict({"celsius": 50.0}))

Files

File	Description
model.mxai	MatrixAI model definition (source of truth)
model.mxtrain	Training contract: dataset, split, loss, optimizer, epochs
params.best.json	Trained parameter weights
model.onnx	ONNX model, opset 17
model_manifest.json	Model metadata, hashes and backend contract
export_manifest.json	Export metadata, tolerance and equivalence check
data_recipe.txt	The recipe the training data was generated from
reproduce.json	Whether this model can be rebuilt, and the digest of each artifact that travels
inference_spec.json	How a raw record maps to the model input (the "tokenizer")
predict.py	Standalone wrapper: raw values in, labelled prediction out
requirements.txt	Minimal deps to run predict.py (numpy + onnxruntime)
example_input.json	A ready-to-run raw example
expected_output.json	The output predict.py should produce for that example
space/	A Hugging Face Space template — yours to publish, or to ignore
README.md	This file

This package is reproducible: reproduce.json carries the recipe, the training contract, the expected dataset sha256 and the exact environment, all of it taken from the capture the core recorded while training this model — and every artifact here matches it. It proves internal consistency, not authorship.

Model info

• Project: CelsiusToKelvin
• Model hash: mxai_fdbe5973e98a1123
• Parameter set: v1_best
• Input: Reading shape [-1, 1] (float32)
• Output: prediction shape [-1]

Equivalence check: PASS (max_abs_diff=3.57e-08, atol=1e-05, n=20)

Advanced: raw onnxruntime access

For most uses prefer predict.py above (it handles normalization and labels). The raw ONNX graph expects an already-normalized float32 vector:

import onnxruntime as ort
import numpy as np

sess = ort.InferenceSession("model.onnx")
x = np.array([[...]], dtype=np.float32)  # shape [batch, 1]
result = sess.run(None, {"Reading": x})[0]  # [-1]

Reproducing this

Everything needed to rebuild this model's data and check it travels inside the package:

What	File	sha256
model	model.mxai	a66357a7adab3ce7…
training contract	model.mxtrain	b7622635f83c8626…
data recipe	data_recipe.txt	7f332dbb7aa59ebf…

• dataset: 300 rows, sha256 5827acf2b2b6058d…
• generation seed: 20260825
• generation mode: coherent
• trained with: environment_sha256 f73756abfe0d07dffabba936f5e66218dd56e5bf3ebc8ee0c507ac69bb3f2f15, matrixai_version 1.6.0, packages {'numpy': '2.4.4', 'onnx': '1.21.0', 'onnxruntime': '1.26.0', 'torch': '2.11.0+cpu'}, platform {'system': 'Linux', 'release': '6.8.0-137-generic', 'machine': 'x86_64', 'platform': 'Linux-6.8.0-137-generic-x86_64-with-glibc2.39'}, python {'version': '3.12.3', 'implementation': 'CPython', 'sys_version': '3.12.3 (main, Jun 19 2026, 12:46:00) [GCC 13.3.0]'}

matrixai verify .              # integrity + rebuild the dataset
matrixai verify . --retrain    # …and train again (slow)
matrixai verify . --json       # same report, machine readable

What you should see: manifest PASS and R1 PASS. With --retrain, also training PASS. R3 reports INCOMPARABLE unless the package publishes metrics with their tolerance — that is a missing datum, not a failure.

Exit codes: 0 nothing failed · 2 something does not match · 3 it could not be checked.

Verifying integrity

import json
with open("model_manifest.json") as f:
    manifest = json.load(f)
assert manifest["model_hash"] == "mxai_fdbe5973e98a1123"
assert manifest["parameter_schema_hash"] == "params_e0a02353b5d9884e"