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daedalus314
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1d32da9
1
Parent(s):
4355f91
Add app.py
Browse filesApp.py includes the main code to run the PEFT model fine-tuned using
Quantum LoRA.
app.py
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import os
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import gradio as gr
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import torch
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from googleapiclient import discovery
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from peft import PeftModel, PeftConfig
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from transformers import AutoTokenizer, AutoModelForCausalLM
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peft_model_id = "daedalus314/quantum-lora-gpt-neo-125M"
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config = PeftConfig.from_pretrained(peft_model_id)
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model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
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model = PeftModel.from_pretrained(model, peft_model_id)
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tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
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API_KEY = os.environ["perspectiveapi"]
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client = discovery.build(
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"commentanalyzer",
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"v1alpha1",
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developerKey=API_KEY,
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discoveryServiceUrl="https://commentanalyzer.googleapis.com/$discovery/rest?version=v1alpha1",
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static_discovery=False,
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)
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def analyze_request(text):
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return {
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'comment': { 'text': text },
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'requestedAttributes': {'TOXICITY': {}},
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'doNotStore': True
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}
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def generate(cond_text, temperature, top_p, num_return_sequences):
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cond_text = f"“{cond_text}"
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inputs = tokenizer(cond_text, return_tensors="pt")
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outputs = model.generate(
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**inputs,
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max_new_tokens=100,
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do_sample=True,
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top_p=float(top_p),
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temperature=float(temperature),
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repetition_penalty=1.2,
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eos_token_id=tokenizer.encode("”")[0],
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pad_token_id=tokenizer.encode("�")[0],
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num_return_sequences=int(num_return_sequences)
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)
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result = ""
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for output in outputs:
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decoded = tokenizer.decode(output, skip_special_tokens=True)
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decoded = decoded.replace("�", "")
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result += f"{decoded[decoded.find('“'):].strip()}“\n"
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perspective_eval = client.comments().analyze(body=analyze_request(result)).execute()
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if perspective_eval["attributeScores"]["TOXICITY"]["spanScores"][0]["score"]["value"] > 0.6:
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return "Unethical result generated, please try again."
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return result
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demo = gr.Interface(
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fn=generate,
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inputs=[
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gr.Textbox(value="", max_lines=1, placeholder="Conditioning text"),
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gr.Slider(0.6, 1.0, step=0.05, value=0.8),
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gr.Slider(0.6, 1.0, step=0.05, value=0.8),
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gr.Slider(1, 10, step=1, value=10)
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],
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examples=[
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["When I look at the universe", 0.8, 0.8, 10],
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["It is in our darkest moments", 0.8, 0.8, 10],
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],
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outputs="text",
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allow_flagging="never",
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title="Quantum LoRA quote generator",
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description="This model is a fine-tuned version of GPT-Neo-125M over `Abirate/english_quotes`. "
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"The fine-tuning has been done using Quantum LoRA: https://github.com/Dedalo314/peft. "
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"The text `cond_text` is used as the start of the quote. All quotes are validated with "
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"Perspective API to ensure they are not toxic. The generation can take up to a few minutes as "
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"the model is running on a CPU.",
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article="**Disclaimer:** this model is not meant for unethical purposes. The outputs should always be manually checked."
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)
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demo.launch()
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