mrq 2cb465018b implicitly load either normal pickled weights or safetensors on loading the model		2024-08-03 23:34:18 -05:00
..
__init__.py	naive model offloading support (handles automatically splitting parts of the model to requested device per memory constraints, either inferred or requested in the yaml, input tensors are automatically migrated to the right device, it SEEMS to work for training under the test trainer when split between GPU and CPU) (this was specifically only because that Flux imagegen model released so I can test it there)	2024-08-01 20:12:06 -05:00
distributed.py	suppress warning on exit about distributed not being cleaned up (because I updated my system)	2024-07-25 16:50:47 -05:00
io.py	implicitly load either normal pickled weights or safetensors on loading the model	2024-08-03 23:34:18 -05:00
pattern.py	oops, kept forgetting to actually pass in lang/tone tokens (despite not really using these at the moment)	2024-07-18 14:18:34 -05:00
sampler.py	added prom-less training / inferencing, some other things	2024-07-22 19:36:07 -05:00
trainer.py	add cap for NAR-len training, to avoid any weird cases in early training where it'll just mess up and generate long lengths	2024-08-03 21:00:32 -05:00
utils.py	oversight with using resize_modules	2024-08-02 20:28:49 -05:00
wrapper.py	changed torch.Tensor().to(device, dtype) to just torch.tensor(..., device, dtype) because it's been bothering my autism that I'm creating tensors then converting rather than creating with the right device/dtype, some 'optimization' to compile the model but it doesnt seem to do anything useful	2024-08-03 22:10:21 -05:00