from huggingface_hub import notebook_login
notebook_login() model.push_to_hub("ybelkada/flan-t5-large-financial-phrasebank-lora", use_auth_token=True) import torch from peft import PeftModel, PeftConfig from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
peft_model_id = "ybelkada/flan-t5-large-financial-phrasebank-lora" config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, torch_dtype="auto", device_map="auto") tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id) model.eval() input_text = "In January-September 2009 , the Group 's net interest income increased to EUR 112.4 mn from EUR 74.3 mn in January-September 2008 ." inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(input_ids=inputs["input_ids"], max_new_tokens=10)
print("input sentence: ", input_text) print(" output prediction: ", tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))