mrq eff180248c decoupled llama backend to avoid any funny changes from transformers, removed other backends since i dont think i'll ever bother using them		2025-02-27 19:00:37 -06:00
..
arch	decoupled llama backend to avoid any funny changes from transformers, removed other backends since i dont think i'll ever bother using them	2025-02-27 19:00:37 -06:00
__init__.py	segregated experimental changes into its own streamlined file to avoid breaking the existing model, and it can pivot to the cleaned up code if it actually works (nothing is working)	2025-02-26 21:26:13 -06:00
ar_nar_v2.py	segregated experimental changes into its own streamlined file to avoid breaking the existing model, and it can pivot to the cleaned up code if it actually works (nothing is working)	2025-02-26 21:26:13 -06:00
ar_nar.py	segregated experimental changes into its own streamlined file to avoid breaking the existing model, and it can pivot to the cleaned up code if it actually works (nothing is working)	2025-02-26 21:26:13 -06:00
base_v2.py	I think I made resp_parallel_training=True faster with loss factoring?	2025-02-26 23:13:32 -06:00
base.py	decoupled llama backend to avoid any funny changes from transformers, removed other backends since i dont think i'll ever bother using them	2025-02-27 19:00:37 -06:00
lora.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