Buckets:
| # LAMB | |
| [LAMB (Layerwise adaptive large batch optimization)](https://hf.co/papers/1904.00962) is an adaptive optimizer designed for training with large batch sizes to accelerate training, combining ideas from `LARS` and `Adam` to automatically scale the learning rate for each layer: | |
| - calculates a *trust ratio* between the weight and gradient norm in a layer and clips the ratio to prevent overly large or small updates | |
| - updates weights with the first and second-moments | |
| ## LAMB[[api-class]][[bitsandbytes.optim.LAMB]] | |
| - **params** (`torch.tensor`) -- | |
| The input parameters to optimize. | |
| - **lr** (`float`, defaults to 1e-3) -- | |
| The learning rate. | |
| - **bias_correction** (`bool`, defaults to `True`) -- | |
| Whether to apply bias correction to the first and second-order moments. | |
| - **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 1e-2) -- | |
| 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. | |
| - **adam_w_mode** (`bool`, defaults to `True`) -- | |
| Whether to use the AdamW variant. | |
| - **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. | |
| - **max_unorm** (`float`, defaults to 1.0) -- | |
| The maximum gradient norm. | |
| Base LAMB optimizer. | |
| ## LAMB8bit[[bitsandbytes.optim.LAMB8bit]] | |
| - **params** (`torch.tensor`) -- | |
| The input parameters to optimize. | |
| - **lr** (`float`, defaults to 1e-3) -- | |
| The learning rate. | |
| - **bias_correction** (`bool`, defaults to `True`) -- | |
| Whether to apply bias correction to the first and second-order moments. | |
| - **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 1e-2) -- | |
| 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 LAMB8bit and must be False. | |
| - **adam_w_mode** (`bool`, defaults to `True`) -- | |
| Whether to use the AdamW variant. | |
| - **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. | |
| - **max_unorm** (`float`, defaults to 1.0) -- | |
| The maximum update norm for trust-ratio clipping. | |
| Note: This parameter is not supported in LAMB8bit and must be left at the | |
| default 1.0. The 8-bit blockwise update does not implement update-norm | |
| clipping; it is honored by the 32-bit LAMB / LAMB32bit optimizers. | |
| 8-bit LAMB optimizer. | |
| ## LAMB32bit[[bitsandbytes.optim.LAMB32bit]] | |
| - **params** (`torch.tensor`) -- | |
| The input parameters to optimize. | |
| - **lr** (`float`, defaults to 1e-3) -- | |
| The learning rate. | |
| - **bias_correction** (`bool`, defaults to `True`) -- | |
| Whether to apply bias correction to the first and second-order moments. | |
| - **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 1e-2) -- | |
| 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. | |
| - **adam_w_mode** (`bool`, defaults to `True`) -- | |
| Whether to use the AdamW variant. | |
| - **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. | |
| - **max_unorm** (`float`, defaults to 1.0) -- | |
| The maximum gradient norm. | |
| 32-bit LAMB optimizer. | |
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