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from dataclasses import dataclass
from transformers import PretrainedConfig
@dataclass
class GPTConfig(PretrainedConfig):
"""
Configuration class for custom GPT model.
"""
model_type = "custom_gpt"
block_size: int = 768
vocab_size: int = 50257
n_layer: int = 8
n_head: int = 8
n_embd: int = 768
dropout: float = 0.1
@classmethod
def from_pretrained(cls, *args, **kwargs):
"""
Override the from_pretrained method to handle custom configuration loading.
"""
return super().from_pretrained(*args, **kwargs) |