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The model is just an untrained architecture. You'd need to spend a lot of money (a) gathering data, and (b) running GPUs to train it. The weights are the fruit
by superb-owl 4y ago
The model is just an untrained architecture. You'd need to spend a lot of money (a) gathering data, and (b) running GPUs to train it.
The weights are the fruition of training. They make the model actually useful.
- rexreed 4y agoWhen you download a trained model for use by Python, I'm assuming the model contains both the architecture (the neural net or even a boosted tree) as well as the weights / tree structure that makes the model actually usable in inference. When organizations release a trained model, I'm assuming that the weights are necessary to make use of that model? If not, then are they not really releasing the model, but just the architecture and training data?
- freeone3000 4y agoUsually not the training data either!
- vdfs 4y agoAs i understand it: - Model is the code - Data is the text/images used for traing - Weights are the training results For example Lucene, models will be the java library, data is text data like wikipedia and weights are the lucene index. if you have all the 3 you can start searching right away, if you have model+data you have to generate the index which can take a lot of time, training/indexing take more than searching or using the model. if you have just he model you need to get your own data and run training on it