+ ܳj 2^RIHt!RR]4t!RR]4t!RR]4t!RR ]4t!R R ]4t!R R ]4tR#))Optimizer2Statec6aa]tRt^ toRV3RlltRtVtV;t#)Adamc  :<\S V`RVVVVVVVV V R7 R#)ai Base Adam optimizer. Arguments: params (`torch.tensor`): The input parameters to optimize. lr (`float`, defaults to 1e-3): The learning rate. betas (`tuple(float, float)`, defaults to (0.9, 0.999)): The beta values are the decay rates of the first and second-order moment 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. amsgrad (`bool`, defaults to `False`): Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. 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. is_paged (`bool`, defaults to `False`): Whether the optimizer is a paged optimizer or not. adamis_pagedNsuper__init__ selfparamslrbetaseps weight_decayamsgrad optim_bitsargs min_8bit_sizer __class__s &&&&&&&&&&&X/home/luca/screen-checking/.venv/lib/python3.14/site-packages/bitsandbytes/optim/adam.pyr Adam.__init__ s6L            gMbP?)g?g+?g:0yE>rF NiF__name__ __module__ __qualname____firstlineno__r __static_attributes____classdictcell__ __classcell__r __classdict__s@@rrr 1 1 rrc6aa]tRt^>toRV3RlltRtVtV;t#)Adam8bitc  <V'd \R4hV^ 8wd \R4h\S V` RVVVVV^VV V R7 R#)a 8-bit Adam optimizer. Arguments: params (`torch.tensor`): The input parameters to optimize. lr (`float`, defaults to 1e-3): The learning rate. betas (`tuple(float, float)`, defaults to (0.9, 0.999)): The beta values are the decay rates of the first and second-order moment 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. amsgrad (`bool`, defaults to `False`): Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. Note: This parameter is not supported in Adam8bit and must be False. optim_bits (`int`, defaults to 32): The number of bits of the optimizer state. Note: This parameter is not used in Adam8bit as it always uses 8-bit optimization. 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. is_paged (`bool`, defaults to `False`): Whether the optimizer is a paged optimizer or not. z&Adam8bit does not support amsgrad=TruezFAdam8bit only supports optim_bits=32 (default value for compatibility)rrN ValueErrorr r r s &&&&&&&&&&&rr Adam8bit.__init__?s[R EF F  ef f           rrrrr's@@rr+r+>< < rr+c6aa]tRt^~toRV3RlltRtVtV;t#) Adam32bitc  :<\S V`RVVVVV^ VV V R7 R#)ak 32-bit Adam optimizer. Arguments: params (`torch.tensor`): The input parameters to optimize. lr (`float`, defaults to 1e-3): The learning rate. betas (`tuple(float, float)`, defaults to (0.9, 0.999)): The beta values are the decay rates of the first and second-order moment 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. amsgrad (`bool`, defaults to `False`): Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. 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. is_paged (`bool`, defaults to `False`): Whether the optimizer is a paged optimizer or not. rrNr r s &&&&&&&&&&&rr Adam32bit.__init__s6L            rrrrr's@@rr2r2~r)rr2c6aa]tRt^toRV3RlltRtVtV;t#) PagedAdamc  :<\S V`RVVVVVVVV RR7 R#)aj Paged Adam optimizer. Arguments: params (`torch.tensor`): The input parameters to optimize. lr (`float`, defaults to 1e-3): The learning rate. betas (`tuple(float, float)`, defaults to (0.9, 0.999)): The beta values are the decay rates of the first and second-order moment 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. amsgrad (`bool`, defaults to `False`): Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. 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. is_paged (`bool`, defaults to `False`): Whether the optimizer is a paged optimizer or not. rTrNr r s &&&&&&&&&&&rr PagedAdam.__init__s6L            rrrrr's@@rr6r6r)rr6c6aa]tRt^toRV3RlltRtVtV;t#) PagedAdam8bitc  <V'd \R4hV^ 8wd \R4h\S V` RVVVVV^VV RR7 R#)a" 8-bit paged Adam optimizer. Arguments: params (`torch.tensor`): The input parameters to optimize. lr (`float`, defaults to 1e-3): The learning rate. betas (`tuple(float, float)`, defaults to (0.9, 0.999)): The beta values are the decay rates of the first and second-order moment 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. amsgrad (`bool`, defaults to `False`): Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. Note: This parameter is not supported in PagedAdam8bit and must be False. optim_bits (`int`, defaults to 32): The number of bits of the optimizer state. Note: This parameter is not used in PagedAdam8bit as it always uses 8-bit optimization. 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. is_paged (`bool`, defaults to `False`): Whether the optimizer is a paged optimizer or not. z+PagedAdam8bit does not support amsgrad=TruezKPagedAdam8bit only supports optim_bits=32 (default value for compatibility)rTrNr-r s &&&&&&&&&&&rr PagedAdam8bit.__init__s[R JK K  jk k           rrrrr's@@rr:r:r0rr:c6aa]tRtRtoRV3RlltRtVtV;t#)PagedAdam32biti(c  :<\S V`RVVVVV^ VV RR7 R#)aq Paged 32-bit Adam optimizer. Arguments: params (`torch.tensor`): The input parameters to optimize. lr (`float`, defaults to 1e-3): The learning rate. betas (`tuple(float, float)`, defaults to (0.9, 0.999)): The beta values are the decay rates of the first and second-order moment 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. amsgrad (`bool`, defaults to `False`): Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. 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. is_paged (`bool`, defaults to `False`): Whether the optimizer is a paged optimizer or not. rTrNr r s &&&&&&&&&&&rr PagedAdam32bit.__init__)s6L            rrrrr's@@rr>r>(r)rr>N)bitsandbytes.optim.optimizerrrr+r2r6r:r>rrrrBsW 92 ?2 j= = @2 2 j2 2 j= O= @2 _2 r