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import os
import gradio as gr
import torch
from googleapiclient import discovery
from peft import PeftModel, PeftConfig
from transformers import AutoTokenizer, AutoModelForCausalLM
peft_model_id = "daedalus314/quantum-lora-gpt-neo-125M"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
model = PeftModel.from_pretrained(model, peft_model_id)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
API_KEY = os.environ["perspectiveapi"]
client = discovery.build(
"commentanalyzer",
"v1alpha1",
developerKey=API_KEY,
discoveryServiceUrl="https://commentanalyzer.googleapis.com/$discovery/rest?version=v1alpha1",
static_discovery=False,
)
def analyze_request(text):
return {
'comment': { 'text': text },
'requestedAttributes': {'TOXICITY': {}},
'doNotStore': True
}
def generate(cond_text, temperature, top_p, num_return_sequences):
cond_text = f"“{cond_text}"
inputs = tokenizer(cond_text, return_tensors="pt")
outputs = model.generate(
**inputs,
max_new_tokens=100,
do_sample=True,
top_p=float(top_p),
temperature=float(temperature),
repetition_penalty=1.2,
eos_token_id=tokenizer.encode("”")[0],
pad_token_id=tokenizer.encode("�")[0],
num_return_sequences=int(num_return_sequences)
)
result = ""
for output in outputs:
decoded = tokenizer.decode(output, skip_special_tokens=True)
decoded = decoded.replace("�", "")
result += f"{decoded[decoded.find('“'):].strip()}“\n"
perspective_eval = client.comments().analyze(body=analyze_request(result)).execute()
if perspective_eval["attributeScores"]["TOXICITY"]["spanScores"][0]["score"]["value"] > 0.6:
return "Unethical result generated, please try again."
return result
demo = gr.Interface(
fn=generate,
inputs=[
gr.Textbox(value="", max_lines=1, placeholder="Conditioning text"),
gr.Slider(0.6, 1.0, step=0.05, value=0.8),
gr.Slider(0.6, 1.0, step=0.05, value=0.8),
gr.Slider(1, 10, step=1, value=10)
],
examples=[
["When I look at the universe", 0.8, 0.8, 10],
["It is in our darkest moments", 0.8, 0.8, 10],
],
outputs="text",
allow_flagging="never",
title="Quantum LoRA quote generator",
description="This model is a fine-tuned version of GPT-Neo-125M over `Abirate/english_quotes`. "
"The fine-tuning has been done using Quantum LoRA: https://github.com/Dedalo314/peft. "
"The text `cond_text` is used as the start of the quote. All quotes are validated with "
"Perspective API to ensure they are not toxic. The generation can take up to a few minutes as "
"the model is running on a CPU.",
article="**Disclaimer:** this model is not meant for unethical purposes. The outputs should always be manually checked."
)
demo.launch()
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