
ONNX standardizes machine learning model representation
Image: Susan Moran, Landsat 7 Science Team and USDA Agricultural Research Service (U.S. Government work), Public domain, via Wikimedia Commons
ONNX standardizes machine learning model representation
ONNX provides a unified format for machine learning models, facilitating interoperability across different frameworks and platforms.
Example
A PyTorch model can be converted to ONNX and then used in a TensorFlow environment.
Remember this
Standardization simplifies the process of deploying models across various platforms and tools.
Text adapted from Wikipedia, licensed under CC BY-SA 4.0.
the embedding layer does: maps discrete token IDs to dense learned vectors
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weight tying does in language models: shares embedding and output projection matrices
Ever wonder how machines understand the sequence of words in a sentence?
MoE models have more parameters but similar compute cost
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to standardize: when you need zero mean and unit variance for gradient-based optimization
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