# Adam

[Adam (Adaptive moment estimation)](https://hf.co/papers/1412.6980) is an adaptive learning rate optimizer, combining ideas from `SGD` with momentum and `RMSprop` to automatically scale the learning rate:

- a weighted average of the past gradients to provide direction (first-moment)
- a weighted average of the *squared* past gradients to adapt the learning rate to each parameter (second-moment)

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.

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

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<docstring><name>class bitsandbytes.optim.Adam</name><anchor>bitsandbytes.optim.Adam</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/adam.py#L9</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.001"}, {"name": "betas", "val": " = (0.9, 0.999)"}, {"name": "eps", "val": " = 1e-08"}, {"name": "weight_decay", "val": " = 0"}, {"name": "amsgrad", "val": " = False"}, {"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>



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>__init__</name><anchor>bitsandbytes.optim.Adam.__init__</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/adam.py#L10</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.001"}, {"name": "betas", "val": " = (0.9, 0.999)"}, {"name": "eps", "val": " = 1e-08"}, {"name": "weight_decay", "val": " = 0"}, {"name": "amsgrad", "val": " = False"}, {"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-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.
- **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 Adam optimizer.




</div></div>

## Adam8bit[[bitsandbytes.optim.Adam8bit]]

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<docstring><name>class bitsandbytes.optim.Adam8bit</name><anchor>bitsandbytes.optim.Adam8bit</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/adam.py#L70</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.001"}, {"name": "betas", "val": " = (0.9, 0.999)"}, {"name": "eps", "val": " = 1e-08"}, {"name": "weight_decay", "val": " = 0"}, {"name": "amsgrad", "val": " = False"}, {"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>



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>__init__</name><anchor>bitsandbytes.optim.Adam8bit.__init__</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/adam.py#L71</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.001"}, {"name": "betas", "val": " = (0.9, 0.999)"}, {"name": "eps", "val": " = 1e-08"}, {"name": "weight_decay", "val": " = 0"}, {"name": "amsgrad", "val": " = False"}, {"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-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.
- **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 Adam optimizer.




</div></div>

## Adam32bit[[bitsandbytes.optim.Adam32bit]]

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<docstring><name>class bitsandbytes.optim.Adam32bit</name><anchor>bitsandbytes.optim.Adam32bit</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/adam.py#L142</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.001"}, {"name": "betas", "val": " = (0.9, 0.999)"}, {"name": "eps", "val": " = 1e-08"}, {"name": "weight_decay", "val": " = 0"}, {"name": "amsgrad", "val": " = False"}, {"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>



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>__init__</name><anchor>bitsandbytes.optim.Adam32bit.__init__</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/adam.py#L143</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.001"}, {"name": "betas", "val": " = (0.9, 0.999)"}, {"name": "eps", "val": " = 1e-08"}, {"name": "weight_decay", "val": " = 0"}, {"name": "amsgrad", "val": " = False"}, {"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-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.
- **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 Adam optimizer.




</div></div>

## PagedAdam[[bitsandbytes.optim.PagedAdam]]

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<docstring><name>class bitsandbytes.optim.PagedAdam</name><anchor>bitsandbytes.optim.PagedAdam</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/adam.py#L203</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.001"}, {"name": "betas", "val": " = (0.9, 0.999)"}, {"name": "eps", "val": " = 1e-08"}, {"name": "weight_decay", "val": " = 0"}, {"name": "amsgrad", "val": " = False"}, {"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>



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>__init__</name><anchor>bitsandbytes.optim.PagedAdam.__init__</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/adam.py#L204</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.001"}, {"name": "betas", "val": " = (0.9, 0.999)"}, {"name": "eps", "val": " = 1e-08"}, {"name": "weight_decay", "val": " = 0"}, {"name": "amsgrad", "val": " = False"}, {"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-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.
- **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>

Paged Adam optimizer.




</div></div>

## PagedAdam8bit[[bitsandbytes.optim.PagedAdam8bit]]

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<docstring><name>class bitsandbytes.optim.PagedAdam8bit</name><anchor>bitsandbytes.optim.PagedAdam8bit</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/adam.py#L264</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.001"}, {"name": "betas", "val": " = (0.9, 0.999)"}, {"name": "eps", "val": " = 1e-08"}, {"name": "weight_decay", "val": " = 0"}, {"name": "amsgrad", "val": " = False"}, {"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>



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>__init__</name><anchor>bitsandbytes.optim.PagedAdam8bit.__init__</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/adam.py#L265</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.001"}, {"name": "betas", "val": " = (0.9, 0.999)"}, {"name": "eps", "val": " = 1e-08"}, {"name": "weight_decay", "val": " = 0"}, {"name": "amsgrad", "val": " = False"}, {"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-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.
- **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 paged Adam optimizer.




</div></div>

## PagedAdam32bit[[bitsandbytes.optim.PagedAdam32bit]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class bitsandbytes.optim.PagedAdam32bit</name><anchor>bitsandbytes.optim.PagedAdam32bit</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/adam.py#L336</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.001"}, {"name": "betas", "val": " = (0.9, 0.999)"}, {"name": "eps", "val": " = 1e-08"}, {"name": "weight_decay", "val": " = 0"}, {"name": "amsgrad", "val": " = False"}, {"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>



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>__init__</name><anchor>bitsandbytes.optim.PagedAdam32bit.__init__</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/adam.py#L337</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": " = 0.001"}, {"name": "betas", "val": " = (0.9, 0.999)"}, {"name": "eps", "val": " = 1e-08"}, {"name": "weight_decay", "val": " = 0"}, {"name": "amsgrad", "val": " = False"}, {"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-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.
- **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>

Paged 32-bit Adam optimizer.




</div></div>

<EditOnGithub source="https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/docs/source/reference/optim/adam.mdx" />