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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
from optimum.intel import OVModelForCausalLM
model_name = "DarwinAnim8or/Pythia-Greentext-1.4b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = OVModelForCausalLM.from_pretrained(model_name, export=True)
def generate(text, length=100, penalty=3, temperature=0.8, topk=40):
input_text = "Write a greentext from 4chan.org. The story should be like a bullet-point list using > as the start of each line. Most greentexts are humorous or absurd in nature. Most greentexts have a twist near the end.\n"
if not text.startswith(">"):
input_text += ">" + text + "\n>"
else:
input_text += text + "\n>"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
input_ids = input_ids[:, :-1] # remove the last token, which is ">"
length = length + input_ids.size(1) # adjust total length
output = model.generate(
input_ids,
max_length=length,
temperature=temperature,
top_k=topk,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=penalty,
early_stopping=True,
)
generated_text = tokenizer.decode(output[:, input_ids.size(1):][0], skip_special_tokens=True)
return generated_text
examples = [
["be me"],
["be going to heaven"],
#["be going to work"],
#["be baking a pie"],
#["come home after another tiring day"],
["be a plague doctor"]
]
demo = gr.Interface(
fn=generate,
inputs=[
gr.inputs.Textbox(lines=5, label="Input Text"),
gr.inputs.Slider(5, 200, label='Length', default=100, step=5),
gr.inputs.Slider(1, 10, label='no repeat ngram size', default=2, step=1),
gr.inputs.Slider(0.0, 1.0, label='Temperature - control randomness', default=0.2, step=0.1),
gr.inputs.Slider(10, 100, label="top_k", default=40, step=10)
],
outputs=gr.outputs.Textbox(label="Generated Text"),
examples=examples,
title="Pythia-Greentext Playground",
description="Using the 1.4b size model. You may need to run it a few times in order to get something good!"
)
demo.launch() |