abvijaykumar/python-fine-tuning

1import gradio as gr2model_name = "vijjuk/codegen-350M-mono-python-18k-alpaca"3demo = gr.load(model_name, src="models")4 5demo.launch()6 7 8 9 10 11#import gradio as gr12#from transformers import AutoTokenizer, AutoModelForCausalLM13 14 15#base_model = AutoModelForCausalLM.from_pretrained(model_name)16#tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)17#tokenizer.pad_token = tokenizer.eos_token18#tokenizer.padding_side = "right"19 20# def query(instruction, input):21#    prompt = f"""### Instruction:22#        Use the Task below and the Input given to write the Response, which is a programming code that can solve the Task.23#       ### Task:24#        {instruction}25#        ### Input:26#        {input}27#        ### Response:28#        """29#    input_ids = tokenizer(prompt, return_tensors="pt", truncation=True)30#    output_base = base_model.generate(input_ids=input_ids, max_new_tokens=500, do_sample=True, top_p=0.9,temperature=0.5)31#    response = "{tokenizer.batch_decode(output_base.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}"32#    return response33 34#inputs = ["text", "text"]35#outputs = "text"36#iface = gr.Interface(fn=query, inputs=inputs, outputs=outputs)37#iface.launch()