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import gradio as gr
import torch
from my_gpt import my_gpt
from tokenizer.tokenizer import BPE
##Load model
model = my_gpt.load_pretrained("model/model_1000_.bin")
# model.to(torch.device("cpu"))
# model.save_pretrained("model/model_1000_cpu.bin")
# exit()
tokenizer = BPE()
def generate(input_text):
tokens = tokenizer.encode(input_text)
gen_ids = model.generate(torch.tensor([tokens]))
output = tokenizer.decode(gen_ids[0].tolist())
return output
iface = gr.Interface(fn=generate,
inputs="text",
outputs="text",
title="NoobGPT - 1000 steps",
description="""This 13M param model is trained for 1000steps only and has seen only 1M tokens. It is not
able to generate perfect sentences/words but has acquired a rudimentary understanding of the English language""")
iface.launch() |