Unverified50% confidenceFactExact time
Model weights are the parameter files saved after model training, can reach tens of GB in size, and need to be downloaded locally before inference can be performed.
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Long-term
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10/13/2026
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VerifiedFine-tuning involves taking an existing large model and further training it with a specially prepared proprietary dataset, thereby changing the model's weight values74% similarUnverifiedIn the command for downloading a single file using ModelScope, the model parameter corresponds to the model repository name, the filename corresponds to the specific model file, and the download directory is represented by ./ for the current folder.73% similarVerifiedModel weights are the parameter values stored after a neural network has been trained; releasing weights publicly means anyone can run, fine-tune, and deeply audit the model locally.73% similarUnverifiedA base model is the original version that has completed pre-training on large-scale data but has not been fine-tuned for a specific task, retaining the most complete parameter space and generalization potential.72% similarUnverifiedOn-device inference adopts the paradigm of 'data stays, model moves': a pre-trained model is downloaded to the local device, inference is completed on the device's NPU or GPU, and input data remains within the secure sandbox.70% similar
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