Update peft_config.py

Getting rid of alora config definition in this repo

Signed-off-by: Greenewald <greenewk@umich.edu>

Author: kgreenewald

tuning/config/peft_config.py

@@ -58,49 +58,6 @@ class LoraConfig:
     bias = "none"
     lora_dropout: float = 0.05
 
-@dataclass
-class aLoraConfig:
-    """
-    This is the configuration class to store the configuration of an Activated LoRA [`aLoraModel`].
-
-    Args:
-        r (`int`):
-            Lora attention dimension (the "rank").
-        target_modules (List[str]]):
-            The names of the modules to apply the adapter to. \
-            If this is specified, only the modules with the specified \
-            names will be replaced. Please specify modules as per model architecture. \
-            If the value is ["all-linear"], \
-            then LORA selects all linear and Conv1D modules as per model architecture, \
-            except for the output layer.
-        lora_alpha (`int`):
-            The alpha parameter for Lora scaling.
-        lora_dropout (`float`):
-            The dropout probability for Lora layers.
-        bias (`str`):
-            Bias type for LoRA. Can be 'none', 'all' or 'lora_only'. \
-            If 'all' or 'lora_only', the corresponding biases will be updated during training. \
-            Be aware that this means that, even when disabling the adapters, the model \
-            will not produce the same output as the base model would have without adaptation.
-        activation_prompt:
-            String which indicates the start of the aLoRA generation.
-    """
-
-    r: int = 8
-    lora_alpha: int = 32
-    target_modules: List[str] = field(
-        default=None,
-        metadata={
-            "help": "The names of the modules to apply LORA to. LORA selects modules which either \
-            completely match or "
-            'end with one of the strings. If the value is ["all-linear"], \
-            then LORA selects all linear and Conv1D '
-            "modules except for the output layer."
-        },
-    )
-    bias = "none"
-    lora_dropout: float = 0.05
-    activation_prompt: str = None
 
 
 @dataclass