app.py

import gradio as gr

from transformers import (
    AutoModelForCausalLM, 
    BitsAndBytesConfig,
    pipeline
)
import torch

quantization_config = BitsAndBytesConfig(llm_int8_enable_fp16_cpu_offload=True)

model_name = "lmsys/vicuna-7b-v1.5"
base_model = AutoModelForCausalLM.from_pretrained(
    model_name,
    low_cpu_mem_usage=True,
    return_dict=True,
    #torch_dtype=torch.float16,
    device_map="cpu",
    load_in_8bit=True,
    quantization_config=quantization_config,
)
new_model = "emya/vicuna-7b-v1.5-steve-jobs-8bit-v1"
model = PeftModel.from_pretrained(base_model, new_model, load_in_8bit=True)

pipe = pipeline("translation", model=model)

def predict(text):
    prompt = f"{text} (Answer in a few sentences)"
    return pipe(prompt)[0]["translation_text"]

demo = gr.Interface(
    fn=predict,
    inputs='text',
    outputs='text',
)

demo.launch()