app.py

import gradio as gr

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("cyberagent/open-calm-7b", device_map="auto", torch_dtype=torch.int8, load_in_8bit=True)
                                                                                                            #torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained("cyberagent/open-calm-7b")

def proc( inputs ):
    with torch.no_grad():
        tokens = model.generate(
            **inputs,
            max_new_tokens=64,  # 生成する長さ. 128 とかでも良い.
            do_sample=True,
            temperature=0.7,  # 生成のランダム性. 高いほど様々な単語が出てくるが関連性は下がる.
            pad_token_id=tokenizer.pad_token_id,
        )
    
    return tokenizer.decode(tokens[0], skip_special_tokens=True)

def greet(name):
    inputs = tokenizer(name, return_tensors="pt").to(model.device)
    outputs = proc( inputs )
    return( outputs )

iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch()