+ ܳjT^RIHt!RR]4t!RR]4t!RR]4tR#) )Optimizer1Statec6aa]tRt^toRV3RlltRtVtV;t#)RMSpropc  <V^8Xd \R4hV'd \R4h\S V` RVVW63VVVV V 4 R#)aI Base RMSprop optimizer. Arguments: params (`torch.tensor`): The input parameters to optimize. lr (`float`, defaults to 1e-2): The learning rate. alpha (`float`, defaults to 0.99): The alpha value is the decay rate of the squared gradients of the optimizer. eps (`float`, defaults to 1e-8): The epsilon value prevents division by zero in the optimizer. weight_decay (`float`, defaults to 0.0): The weight decay value for the optimizer. momentum (`float`, defaults to 0): The momentum value speeds up the optimizer by taking bigger steps. centered (`bool`, defaults to `False`): Whether the gradients are normalized by the variance. If `True`, it can help training at the expense of additional compute. optim_bits (`int`, defaults to 32): The number of bits of the optimizer state. args (`object`, defaults to `None`): An object with additional arguments. min_8bit_size (`int`, defaults to 4096): The minimum number of elements of the parameter tensors for 8-bit optimization. )RMSprop with alpha==0.0 is not supported!"Centered RMSprop is not supported!rmspropNNotImplementedErrorsuper__init__) selfparamslralphaeps weight_decaymomentumcentered optim_bitsargs min_8bit_size __class__s &&&&&&&&&&&[/home/luca/screen-checking/.venv/lib/python3.14/site-packages/bitsandbytes/optim/rmsprop.pyr RMSprop.__init__ sSL A:%&QR R %&JK K            ) {Gz?Gz?:0yE>rrF N__name__ __module__ __qualname____firstlineno__r __static_attributes____classdictcell__ __classcell__r __classdict__s@@rrr4 4 rrc6aa]tRt^@toRV3RlltRtVtV;t#) RMSprop8bitc  <V^8Xd \R4hV'd \R4h\S V` RVVW63VV^VV 4 R#)a 8-bit RMSprop optimizer. Arguments: params (`torch.tensor`): The input parameters to optimize. lr (`float`, defaults to 1e-2): The learning rate. alpha (`float`, defaults to 0.99): The alpha value is the decay rate of the squared gradients of the optimizer. eps (`float`, defaults to 1e-8): The epsilon value prevents division by zero in the optimizer. weight_decay (`float`, defaults to 0.0): The weight decay value for the optimizer. momentum (`float`, defaults to 0): The momentum value speeds up the optimizer by taking bigger steps. centered (`bool`, defaults to `False`): Whether the gradients are normalized by the variance. If `True`, it can help training at the expense of additional compute. args (`object`, defaults to `None`): An object with additional arguments. min_8bit_size (`int`, defaults to 4096): The minimum number of elements of the parameter tensors for 8-bit optimization. rrr Nr rrrrrrrrrrrs &&&&&&&&&&rr RMSprop8bit.__init__AsSF A:%&QR R %&JK K           rrrrr rrFNr"r#r+s@@rr/r/@s1 1 rr/c6aa]tRt^utoRV3RlltRtVtV;t#) RMSprop32bitc  <V^8Xd \R4hV'd \R4h\S V` RVVW63VV^ VV 4 R#)aK 32-bit RMSprop optimizer. Arguments: params (`torch.tensor`): The input parameters to optimize. lr (`float`, defaults to 1e-2): The learning rate. alpha (`float`, defaults to 0.99): The alpha value is the decay rate of the squared gradients of the optimizer. eps (`float`, defaults to 1e-8): The epsilon value prevents division by zero in the optimizer. weight_decay (`float`, defaults to 0.0): The weight decay value for the optimizer. momentum (`float`, defaults to 0): The momentum value speeds up the optimizer by taking bigger steps. centered (`bool`, defaults to `False`): Whether the gradients are normalized by the variance. If `True`, it can help training at the expense of additional compute. optim_bits (`int`, defaults to 32): The number of bits of the optimizer state. args (`object`, defaults to `None`): An object with additional arguments. min_8bit_size (`int`, defaults to 4096): The minimum number of elements of the parameter tensors for 8-bit optimization. rrr Nr r1s &&&&&&&&&&rr RMSprop32bit.__init__vsSL A:%&QR R %&JK K            rrr3r#r+s@@rr5r5ur-rr5N)bitsandbytes.optim.optimizerrrr/r5rrrr9s0 95 o5 p2 /2 j5 ?5 r