The script throws an out of memory error on the non-lora model forward pass. I can print GPU memory immediately after loading the model and notice each GPU has 62.7 GB of memory allocated, except GPU 7, which has 120.9 GB (out of 140.) Ideally, the weights should be distributed evenly. We can specify which weights go where with device_map. You might wonder why device_map=’auto’ distributes weights so unevenly. I certainly did, but could not find a satisfactory answer and am convinced it would be trivial to distribute the weights relatively evenly.
Александра Синицына (Ночной линейный редактор)，更多细节参见wps
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可如今，这个问题变得复杂起来。
I'm very happy now that NetBSD can reach great speeds and I can't wait to