bitsandbytes/optim/lion.py [64:105]:
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    def __init__(
        self,
        params,
        lr=1e-4,
        betas=(0.9, 0.99),
        weight_decay=0,
        args=None,
        min_8bit_size=4096,
        percentile_clipping=100,
        block_wise=True,
        is_paged=False,
    ):
        """
        8-bit Lion optimizer.

        Arguments:
            params (`torch.tensor`):
                The input parameters to optimize.
            lr (`float`, defaults to 1e-4):
                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.
            weight_decay (`float`, defaults to 0):
                The weight decay value for the optimizer.
            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.
            percentile_clipping (`int`, defaults to 100):
                Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
            block_wise (`bool`, defaults to `True`):
                Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
            is_paged (`bool`, defaults to `False`):
                Whether the optimizer is a paged optimizer or not.
        """
        super().__init__(
            "lion",
            params,
            lr,
            betas,
            0.0,
            weight_decay,
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bitsandbytes/optim/lion.py [116:157]:
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    def __init__(
        self,
        params,
        lr=1e-4,
        betas=(0.9, 0.99),
        weight_decay=0,
        args=None,
        min_8bit_size=4096,
        percentile_clipping=100,
        block_wise=True,
        is_paged=False,
    ):
        """
        32-bit Lion optimizer.

        Arguments:
            params (`torch.tensor`):
                The input parameters to optimize.
            lr (`float`, defaults to 1e-4):
                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.
            weight_decay (`float`, defaults to 0):
                The weight decay value for the optimizer.
            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.
            percentile_clipping (`int`, defaults to 100):
                Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
            block_wise (`bool`, defaults to `True`):
                Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.
            is_paged (`bool`, defaults to `False`):
                Whether the optimizer is a paged optimizer or not.
        """
        super().__init__(
            "lion",
            params,
            lr,
            betas,
            0.0,
            weight_decay,
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