Delete main

main

@@ -1,21 +0,0 @@
-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))