# 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]] - **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. Base Adam optimizer. ## Adam8bit[[bitsandbytes.optim.Adam8bit]] - **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. 8-bit Adam optimizer. ## Adam32bit[[bitsandbytes.optim.Adam32bit]] - **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. 32-bit Adam optimizer. ## PagedAdam[[bitsandbytes.optim.PagedAdam]] - **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. Paged Adam optimizer. ## PagedAdam8bit[[bitsandbytes.optim.PagedAdam8bit]] - **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. 8-bit paged Adam optimizer. ## PagedAdam32bit[[bitsandbytes.optim.PagedAdam32bit]] - **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. Paged 32-bit Adam optimizer.