Buckets:
AdEMAMix
AdEMAMix is a variant of the Adam optimizer.
bitsandbytes also supports paged optimizers which take advantage of CUDAs unified memory to transfer memory from the GPU to the CPU when GPU memory is exhausted.
AdEMAMix[[api-class]][[bitsandbytes.optim.AdEMAMix]]
bitsandbytes.optim.AdEMAMix[[bitsandbytes.optim.AdEMAMix]]
bitsandbytes.optim.AdEMAMix(params: Iterable, lr: float = 0.001, betas: tuple = (0.9, 0.999, 0.9999), alpha: float = 5.0, t_alpha: typing.Optional[int] = None, t_beta3: typing.Optional[int] = None, eps: float = 1e-08, weight_decay: float = 0.01, optim_bits: typing.Literal[8, 32] = 32, min_8bit_size: int = 4096, is_paged: bool = False)
init[[bitsandbytes.optim.AdEMAMix.init]]
__init__(params: Iterable, lr: float = 0.001, betas: tuple = (0.9, 0.999, 0.9999), alpha: float = 5.0, t_alpha: typing.Optional[int] = None, t_beta3: typing.Optional[int] = None, eps: float = 1e-08, weight_decay: float = 0.01, optim_bits: typing.Literal[8, 32] = 32, min_8bit_size: int = 4096, is_paged: bool = False)
AdEMAMix8bit[[bitsandbytes.optim.AdEMAMix8bit]]
bitsandbytes.optim.AdEMAMix8bit[[bitsandbytes.optim.AdEMAMix8bit]]
bitsandbytes.optim.AdEMAMix8bit(params: Iterable, lr: float = 0.001, betas: tuple = (0.9, 0.999, 0.9999), alpha: float = 5.0, t_alpha: typing.Optional[int] = None, t_beta3: typing.Optional[int] = None, eps: float = 1e-08, weight_decay: float = 0.01, min_8bit_size: int = 4096, is_paged: bool = False)
init[[bitsandbytes.optim.AdEMAMix8bit.init]]
__init__(params: Iterable, lr: float = 0.001, betas: tuple = (0.9, 0.999, 0.9999), alpha: float = 5.0, t_alpha: typing.Optional[int] = None, t_beta3: typing.Optional[int] = None, eps: float = 1e-08, weight_decay: float = 0.01, min_8bit_size: int = 4096, is_paged: bool = False)
AdEMAMix32bit[[bitsandbytes.optim.AdEMAMix32bit]]
bitsandbytes.optim.AdEMAMix32bit[[bitsandbytes.optim.AdEMAMix32bit]]
bitsandbytes.optim.AdEMAMix32bit(params: Iterable, lr: float = 0.001, betas: tuple = (0.9, 0.999, 0.9999), alpha: float = 5.0, t_alpha: typing.Optional[int] = None, t_beta3: typing.Optional[int] = None, eps: float = 1e-08, weight_decay: float = 0.01, min_8bit_size: int = 4096, is_paged: bool = False)
init[[bitsandbytes.optim.AdEMAMix32bit.init]]
__init__(params: Iterable, lr: float = 0.001, betas: tuple = (0.9, 0.999, 0.9999), alpha: float = 5.0, t_alpha: typing.Optional[int] = None, t_beta3: typing.Optional[int] = None, eps: float = 1e-08, weight_decay: float = 0.01, min_8bit_size: int = 4096, is_paged: bool = False)
PagedAdEMAMix[[bitsandbytes.optim.PagedAdEMAMix]]
bitsandbytes.optim.PagedAdEMAMix[[bitsandbytes.optim.PagedAdEMAMix]]
bitsandbytes.optim.PagedAdEMAMix(params: Iterable, lr: float = 0.001, betas: tuple = (0.9, 0.999, 0.9999), alpha: float = 5.0, t_alpha: typing.Optional[int] = None, t_beta3: typing.Optional[int] = None, eps: float = 1e-08, weight_decay: float = 0.01, optim_bits: typing.Literal[8, 32] = 32, min_8bit_size: int = 4096)
init[[bitsandbytes.optim.PagedAdEMAMix.init]]
__init__(params: Iterable, lr: float = 0.001, betas: tuple = (0.9, 0.999, 0.9999), alpha: float = 5.0, t_alpha: typing.Optional[int] = None, t_beta3: typing.Optional[int] = None, eps: float = 1e-08, weight_decay: float = 0.01, optim_bits: typing.Literal[8, 32] = 32, min_8bit_size: int = 4096)
PagedAdEMAMix8bit[[bitsandbytes.optim.PagedAdEMAMix8bit]]
bitsandbytes.optim.PagedAdEMAMix8bit[[bitsandbytes.optim.PagedAdEMAMix8bit]]
bitsandbytes.optim.PagedAdEMAMix8bit(params: Iterable, lr: float = 0.001, betas: tuple = (0.9, 0.999, 0.9999), alpha: float = 5.0, t_alpha: typing.Optional[int] = None, t_beta3: typing.Optional[int] = None, eps: float = 1e-08, weight_decay: float = 0.01, min_8bit_size: int = 4096)
init[[bitsandbytes.optim.PagedAdEMAMix8bit.init]]
__init__(params: Iterable, lr: float = 0.001, betas: tuple = (0.9, 0.999, 0.9999), alpha: float = 5.0, t_alpha: typing.Optional[int] = None, t_beta3: typing.Optional[int] = None, eps: float = 1e-08, weight_decay: float = 0.01, min_8bit_size: int = 4096)
PagedAdEMAMix32bit[[bitsandbytes.optim.PagedAdEMAMix32bit]]
bitsandbytes.optim.PagedAdEMAMix32bit[[bitsandbytes.optim.PagedAdEMAMix32bit]]
bitsandbytes.optim.PagedAdEMAMix32bit(params: Iterable, lr: float = 0.001, betas: tuple = (0.9, 0.999, 0.9999), alpha: float = 5.0, t_alpha: typing.Optional[int] = None, t_beta3: typing.Optional[int] = None, eps: float = 1e-08, weight_decay: float = 0.01, min_8bit_size: int = 4096)
init[[bitsandbytes.optim.PagedAdEMAMix32bit.init]]
__init__(params: Iterable, lr: float = 0.001, betas: tuple = (0.9, 0.999, 0.9999), alpha: float = 5.0, t_alpha: typing.Optional[int] = None, t_beta3: typing.Optional[int] = None, eps: float = 1e-08, weight_decay: float = 0.01, min_8bit_size: int = 4096)
Xet Storage Details
- Size:
- 6.49 kB
- Xet hash:
- 93f329cb8a162bd7a3715c1279ca58b70d8a340c56253ac2bdf1a7a1cff1d760
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.