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Here's an optimized version of your code that incorporates these strategies: ```python import torch from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from concurrent.futures import ThreadPoolExecutor, as_completed class ReformulationModel: def __init__(self): self.model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") self.tokenizer = AutoTokenizer.from_pretrained("t5-base") def reformulate(self, query): inputs = self.tokenizer(query, return_tensors="pt") outputs = self.model.generate(**inputs) return self.tokenizer.decode(outputs[0], skip_special_tokens=True) def batch_reformulate(self, queries): inputs = self.tokenizer(queries, return_tensors="pt", padding=True, truncation=True) outputs = self.model.generate(**inputs) return [self.tokenizer.decode(output, skip_special_tokens=True) for output in outputs] # Example usage: model = ReformulationModel() def process_queries(queries): with ThreadPoolExecutor(max_workers=10) as executor: futures = [executor.submit(model.batch_reformulate, queries[i:i+100]) for i in range(0, len(queries), 100)] results = []