import gradio as gr import torch from peft import PeftModel, PeftConfig from transformers import AutoTokenizer, AutoModelForCausalLM #Loading model device = "cuda" if torch.cuda.is_available() else "cpu" model_path = "parsanna17/finetune_starcoder2_with_R_data" checkpoint = "bigcode/starcoder2-3b" config = PeftConfig.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained(checkpoint,device_map=device, torch_dtype=torch.bfloat16) model = PeftModel.from_pretrained(model, model_path).to(device) tokenizer = AutoTokenizer.from_pretrained(checkpoint) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token # def remove_header_trailer(input): # text = input.split() # start=0 # end=0 # i=0 # while i