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from transformers import PretrainedConfig, LlamaConfig
from typing import List, Callable
import torch
# class LLama3_SAE_Config(PretrainedConfig):
class LLama3_SAE_Config(LlamaConfig):
model_type = "llama3_SAE"
def __init__(
self,
# hf_token: str = "",
# base_model_config: LlamaConfig = None,
base_model_name: str = "",
hook_block_num: int = 25,
n_latents: int = 12288,
n_inputs: int = 4096,
activation: str = "relu",
activation_k: int = 64,
site: str = "mlp",
tied: bool = False,
normalize: bool = False,
mod_features: List[int] = None,
mod_threshold: List[int] = None,
mod_replacement: List[int] = None,
mod_scaling: List[int] = None,
**kwargs,
):
# self.hf_token = hf_token
# self.base_model_config = base_model_config
self.base_model_name = base_model_name
self.hook_block_num = hook_block_num
self.n_latents = n_latents
self.n_inputs = n_inputs
self.activation = activation
self.activation_k = activation_k
self.site = site
self.tied = tied
self.normalize = normalize
self.mod_features = mod_features
self.mod_threshold = mod_threshold
self.mod_replacement = mod_replacement
self.mod_scaling = mod_scaling
super().__init__(**kwargs)
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