custom_model.py

import torch
import torch.nn as nn
from transformers import PreTrainedModel, PretrainedConfig

# Define the model configuration
class SimpleNNConfig(PretrainedConfig):
    model_type = "simple_nn"

    def __init__(self, hidden_size=16, num_labels=1, **kwargs):
        super().__init__(**kwargs)
        self.hidden_size = hidden_size
        self.num_labels = num_labels

# Define the model architecture
class SimpleNN(PreTrainedModel):
    config_class = SimpleNNConfig

    def __init__(self, config):
        super().__init__(config)
        self.fc1 = nn.Linear(1, config.hidden_size)
        self.fc2 = nn.Linear(config.hidden_size, config.num_labels)
    
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
        config = SimpleNNConfig()
        model = cls(config)
        model.load_state_dict(torch.load(pretrained_model_name_or_path, map_location=torch.device("cpu")))
        return model