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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

device = "cuda" tokenizer = AutoTokenizer.from_pretrained("Ateeqq/Text-Rewriter-Paraphraser", token='your_token') model = AutoModelForSeq2SeqLM.from_pretrained("Ateeqq/Text-Rewriter-Paraphraser", token='your_token').to(device)

def generate_title(text): input_ids = tokenizer(f'paraphraser: {text}', return_tensors="pt", padding="longest", truncation=True, max_length=64).input_ids.to(device) outputs = model.generate( input_ids, num_beams=4, num_beam_groups=4, num_return_sequences=4, repetition_penalty=10.0, diversity_penalty=3.0, no_repeat_ngram_size=2, temperature=0.8, max_length=64 ) return tokenizer.batch_decode(outputs, skip_special_tokens=True)

text = 'By leveraging prior model training through transfer learning, fine-tuning can reduce the amount of expensive computing power and labeled data needed to obtain large models tailored to niche use cases and business needs.' generate_title(text)