license:unknown region:us
---
license: unknown
---
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))