| """Miscovery model implementation""" | |
| from transformers.modeling_utils import PreTrainedModel | |
| from transformers.modeling_outputs import Seq2SeqLMOutput | |
| import torch | |
| import torch.nn as nn | |
| from .configuration_miscovery import CustomTransformerConfig | |
| # Import the actual model architecture | |
| # This is a simplified placeholder that should be replaced with your actual model code | |
| class CustomTransformerModel(PreTrainedModel): | |
| config_class = CustomTransformerConfig | |
| main_input_name = "input_ids" | |
| def __init__(self, config): | |
| super().__init__(config) | |
| # Initialize model components | |
| # This will need to be replaced with your actual model architecture | |
| self.model = None # Your model implementation here | |
| def forward( | |
| self, | |
| input_ids=None, | |
| decoder_input_ids=None, | |
| attention_mask=None, | |
| decoder_attention_mask=None, | |
| labels=None, | |
| **kwargs | |
| ): | |
| # Forward pass implementation | |
| # This will need to be replaced with your actual forward method | |
| return Seq2SeqLMOutput( | |
| loss=None, | |
| logits=None, | |
| ) | |
| def prepare_inputs_for_generation( | |
| self, | |
| decoder_input_ids, | |
| past_key_values=None, | |
| attention_mask=None, | |
| use_cache=None, | |
| encoder_outputs=None, | |
| **kwargs | |
| ): | |
| # Prepare inputs implementation for generation | |
| # This will need to be replaced with your actual method | |
| pass | |