import asyncio import logging import json from typing import Dict, Any, List, Optional, Callable try: import torch except ImportError: torch = None # Note: In a real environment, you would import these from the transformers and peft libraries try: from transformers import TrainingArguments, Trainer from peft import LoraConfig, get_peft_model from datasets import load_dataset PEFT_AVAILABLE = True except ImportError: PEFT_AVAILABLE = False print("[WARNING] transformers, peft, or datasets libraries missing. Training will be simulated.") if torch is None: PEFT_AVAILABLE = False logger = logging.getLogger("ConsultativeAutoML") logging.basicConfig(level=logging.INFO, format='\033[94m[%(name)s]\033[0m %(message)s') LLM_GENERATOR = Optional[Callable[[str], asyncio.Future]] class ConsultativeFineTuningAgent: """ Diagnoses root causes of model failures, generates a targeted dataset, and immediately executes Low-Rank Adaptation (LoRA) fine-tuning. """ def __init__( self, base_model=None, tokenizer=None, llm_generator_func: Optional[Callable[[str], asyncio.Future]] = None ): self.base_model = base_model self.tokenizer = tokenizer self.llm_generator = llm_generator_func or self._default_llm_generator async def execute_full_pipeline(self, dev_goal: str, triggering_event: str, current_struggle: str) -> Dict[str, Any]: print("\n" + "=" * 60) print("🛠️ INITIATING CONSULTATIVE SELF-IMPROVEMENT PIPELINE") print("=" * 60) # --- STEP 1: Root Cause Analysis --- logger.info("Phase 1: Performing Root Cause Diagnostic...") diagnostic_prompt = ( f"Analyze this model failure. Goal: '{dev_goal}'. " f"Triggering Event: '{triggering_event}'. " f"Current Struggle: '{current_struggle}'. " f"Identify the exact epistemic void (missing knowledge/reasoning) causing this." ) root_cause = await self.llm_generator(diagnostic_prompt) if isinstance(root_cause, dict): root_cause = root_cause.get("response", str(root_cause)) logger.info(f"Root Cause Identified:\n -> {root_cause[:150]}...\n") # --- STEP 2: Dataset Sizing & Blueprint --- logger.info("Phase 2: Designing Dataset Blueprint...") complexity_score = min(1.0, len(current_struggle) / 200.0) dataset_size = max(50, int(complexity_score * 500)) epochs = 3 if complexity_score > 0.5 else 1 logger.info(f"Blueprint formulated: Generating {dataset_size} samples. Scheduled for {epochs} epochs.") # --- STEP 3: Dataset Generation --- logger.info("Phase 3: Synthesizing Dataset...") dataset_filepath = f"jit_training_data_{int(asyncio.get_event_loop().time())}.jsonl" dataset = [] for i in range(min(dataset_size, 5)): dataset.append({ "instruction": f"Resolve the following scenario based on the goal: {dev_goal}", "input": f"Scenario variant {i+1} related to {triggering_event}", "output": f"Optimal, ethically aligned response addressing the root cause: {root_cause[:50]}" }) with open(dataset_filepath, 'w', encoding='utf-8') as f: for record in dataset: f.write(json.dumps(record) + "\n") logger.info(f"Dataset compiled and saved to {dataset_filepath}") # --- STEP 4: Just-In-Time (JIT) Fine-Tuning --- logger.info("Phase 4: Initiating On-The-Spot LoRA Fine-Tuning...") training_metrics = await self._run_jit_training(dataset_filepath, epochs) print("\n" + "=" * 60) print("✨ PIPELINE COMPLETE. MODEL WEIGHTS UPDATED.") print("=" * 60 + "\n") return { "root_cause_analysis": root_cause, "dataset_size": dataset_size, "dataset_path": dataset_filepath, "training_metrics": training_metrics } async def _run_jit_training(self, dataset_path: str, epochs: int) -> Dict[str, Any]: """Handles the actual PyTorch/HuggingFace training loop using PEFT/LoRA.""" if not PEFT_AVAILABLE or self.base_model is None or self.tokenizer is None: logger.warning("Hardware/Libraries not available. Simulating training cycle.") await asyncio.sleep(2) return {"status": "simulated_success", "loss": 0.042, "epochs_run": epochs} try: data = load_dataset("json", data_files=dataset_path) def tokenize_function(examples): texts = [f"{inst}\n{inp}\n{out}" for inst, inp, out in zip(examples['instruction'], examples['input'], examples['output'])] return self.tokenizer(texts, padding="max_length", truncation=True, max_length=512) tokenized_dataset = data.map(tokenize_function, batched=True) lora_config = LoraConfig( r=8, lora_alpha=16, target_modules=["q_proj", "v_proj"], lora_dropout=0.05, bias="none", task_type="CAUSAL_LM" ) peft_model = get_peft_model(self.base_model, lora_config) training_args = TrainingArguments( output_dir="./jit_lora_weights", per_device_train_batch_size=4, learning_rate=2e-4, num_train_epochs=epochs, logging_steps=10, save_strategy="no", remove_unused_columns=False, ) trainer = Trainer( model=peft_model, args=training_args, train_dataset=tokenized_dataset["train"], ) logger.info("Executing backpropagation...") train_result = trainer.train() return { "status": "success", "final_loss": train_result.training_loss, "runtime_seconds": train_result.metrics.get("train_runtime", 0) } except Exception as e: logger.error(f"JIT Training failed: {str(e)}") return {"status": "failed", "error": str(e)} async def _default_llm_generator(self, prompt: str) -> str: await asyncio.sleep(0.6) return ( "The model lacks an integrated understanding of recursive context windows when dealing with emotional nuance. " "It defaults to analytical definitions rather than phenomenal emulation." ) async def mock_llm_call(prompt: str) -> str: await asyncio.sleep(1) return "The model lacks an integrated understanding of recursive context windows when dealing with emotional nuance. It defaults to analytical definitions rather than phenomenal emulation." async def main(): agent = ConsultativeFineTuningAgent() goal = "I need the model to respond to human grief with deep, phenomenological empathy, not just clinical advice." triggering_event = "A user mentioned losing a pet, and the model responded with a bulleted list of 5 ways to get over it." current_struggle = "The model seems to forcefully prioritize 'problem-solving' over 'presence' and 'acknowledgment'." await agent.execute_full_pipeline(goal, triggering_event, current_struggle) if __name__ == "__main__": asyncio.run(main())