| # -*- coding: utf-8 -*- | |
| from transformers import PreTrainedModel | |
| from configuration_autonexus import AutoNexusConfig | |
| import torch | |
| try: | |
| from agentworld_ultra import AutoNexusCognitiveEngine | |
| except ImportError: | |
| AutoNexusCognitiveEngine = None | |
| class AutoNexusModel(PreTrainedModel): | |
| config_class = AutoNexusConfig | |
| def __init__(self, config): | |
| super().__init__(config) | |
| self.config = config | |
| # We wrap the Cognitive Engine inside the standard HF PreTrainedModel | |
| if AutoNexusCognitiveEngine is not None: | |
| self.engine = AutoNexusCognitiveEngine() | |
| else: | |
| self.engine = None | |
| # Dummy parameter to satisfy PyTorch module requirements | |
| self.dummy_param = torch.nn.Parameter(torch.empty(0)) | |
| def forward(self, input_ids=None, inputs_embeds=None, objective=None, **kwargs): | |
| """ | |
| Standard forward pass. | |
| Accepts a string 'objective' and executes the Tier-0 cognitive loop. | |
| """ | |
| if objective is None: | |
| raise ValueError("AutoNexus requires an 'objective' string to execute its cognitive loop.") | |
| if self.engine is None: | |
| raise RuntimeError("AutoNexusCognitiveEngine backend missing.") | |
| response = self.engine.autonomous_loop(objective, max_iterations=self.config.max_iterations) | |
| # Return as a dictionary mimicking standard HF outputs | |
| return {"output_text": response} | |
| def generate(self, objective: str, **kwargs): | |
| """ | |
| Override standard generate to trigger the autonomous hyper-reflexive engine. | |
| """ | |
| return self.forward(objective=objective) | |
| # Register the model with Hugging Face auto classes if needed | |
| # AutoConfig.register("autonexus", AutoNexusConfig) | |
| # AutoModel.register(AutoNexusConfig, AutoNexusModel) | |
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