# Lion

[Lion (Evolved Sign Momentum)](https://hf.co/papers/2302.06675) is a unique optimizer that uses the sign of the gradient to determine the update direction of the momentum. This makes Lion more memory-efficient and faster than `AdamW` which tracks and store the first and second-order moments.

## Lion[[api-class]][[bitsandbytes.optim.Lion]]

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<docstring><name>class bitsandbytes.optim.Lion</name><anchor>bitsandbytes.optim.Lion</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/lion.py#L8</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.0001"}, {"name": "betas", "val": " = (0.9, 0.99)"}, {"name": "weight_decay", "val": " = 0"}, {"name": "optim_bits", "val": " = 32"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}, {"name": "is_paged", "val": " = False"}]</parameters></docstring>



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<docstring><name>__init__</name><anchor>bitsandbytes.optim.Lion.__init__</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/lion.py#L9</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.0001"}, {"name": "betas", "val": " = (0.9, 0.99)"}, {"name": "weight_decay", "val": " = 0"}, {"name": "optim_bits", "val": " = 32"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}, {"name": "is_paged", "val": " = False"}]</parameters><paramsdesc>- **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.
- **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.
- **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.</paramsdesc><paramgroups>0</paramgroups></docstring>

Base Lion optimizer.




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## Lion8bit[[bitsandbytes.optim.Lion8bit]]

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<docstring><name>class bitsandbytes.optim.Lion8bit</name><anchor>bitsandbytes.optim.Lion8bit</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/lion.py#L63</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.0001"}, {"name": "betas", "val": " = (0.9, 0.99)"}, {"name": "weight_decay", "val": " = 0"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}, {"name": "is_paged", "val": " = False"}]</parameters></docstring>



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<docstring><name>__init__</name><anchor>bitsandbytes.optim.Lion8bit.__init__</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/lion.py#L64</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.0001"}, {"name": "betas", "val": " = (0.9, 0.99)"}, {"name": "weight_decay", "val": " = 0"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}, {"name": "is_paged", "val": " = False"}]</parameters><paramsdesc>- **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.</paramsdesc><paramgroups>0</paramgroups></docstring>

8-bit Lion optimizer.




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## Lion32bit[[bitsandbytes.optim.Lion32bit]]

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<docstring><name>class bitsandbytes.optim.Lion32bit</name><anchor>bitsandbytes.optim.Lion32bit</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/lion.py#L115</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.0001"}, {"name": "betas", "val": " = (0.9, 0.99)"}, {"name": "weight_decay", "val": " = 0"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}, {"name": "is_paged", "val": " = False"}]</parameters></docstring>



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<docstring><name>__init__</name><anchor>bitsandbytes.optim.Lion32bit.__init__</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/lion.py#L116</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.0001"}, {"name": "betas", "val": " = (0.9, 0.99)"}, {"name": "weight_decay", "val": " = 0"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}, {"name": "is_paged", "val": " = False"}]</parameters><paramsdesc>- **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.</paramsdesc><paramgroups>0</paramgroups></docstring>

32-bit Lion optimizer.




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## PagedLion[[bitsandbytes.optim.PagedLion]]

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<docstring><name>class bitsandbytes.optim.PagedLion</name><anchor>bitsandbytes.optim.PagedLion</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/lion.py#L167</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.0001"}, {"name": "betas", "val": " = (0.9, 0.99)"}, {"name": "weight_decay", "val": " = 0"}, {"name": "optim_bits", "val": " = 32"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}]</parameters></docstring>



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<docstring><name>__init__</name><anchor>bitsandbytes.optim.PagedLion.__init__</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/lion.py#L168</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.0001"}, {"name": "betas", "val": " = (0.9, 0.99)"}, {"name": "weight_decay", "val": " = 0"}, {"name": "optim_bits", "val": " = 32"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}]</parameters><paramsdesc>- **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.
- **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.
- **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.</paramsdesc><paramgroups>0</paramgroups></docstring>

Paged Lion optimizer.




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## PagedLion8bit[[bitsandbytes.optim.PagedLion8bit]]

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<docstring><name>class bitsandbytes.optim.PagedLion8bit</name><anchor>bitsandbytes.optim.PagedLion8bit</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/lion.py#L219</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.0001"}, {"name": "betas", "val": " = (0.9, 0.99)"}, {"name": "weight_decay", "val": " = 0"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}]</parameters></docstring>



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<docstring><name>__init__</name><anchor>bitsandbytes.optim.PagedLion8bit.__init__</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/lion.py#L220</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.0001"}, {"name": "betas", "val": " = (0.9, 0.99)"}, {"name": "weight_decay", "val": " = 0"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}]</parameters><paramsdesc>- **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.
- **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.
- **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.</paramsdesc><paramgroups>0</paramgroups></docstring>

Paged 8-bit Lion optimizer.




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## PagedLion32bit[[bitsandbytes.optim.PagedLion32bit]]

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<docstring><name>class bitsandbytes.optim.PagedLion32bit</name><anchor>bitsandbytes.optim.PagedLion32bit</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/lion.py#L270</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.0001"}, {"name": "betas", "val": " = (0.9, 0.99)"}, {"name": "weight_decay", "val": " = 0"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}]</parameters></docstring>



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<docstring><name>__init__</name><anchor>bitsandbytes.optim.PagedLion32bit.__init__</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/lion.py#L271</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.0001"}, {"name": "betas", "val": " = (0.9, 0.99)"}, {"name": "weight_decay", "val": " = 0"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}]</parameters><paramsdesc>- **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.
- **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.
- **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.</paramsdesc><paramgroups>0</paramgroups></docstring>

Paged 32-bit Lion optimizer.




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