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import gradio as gr
from transformers import GPT2Tokenizer, GPT2LMHeadModel
# Load the model and tokenizer
model_name = "migueldeguzmandev/GPT2XL_RLLMv3-PTT-10"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)
# Set the pad token ID to the EOS token ID
model.config.pad_token_id = model.config.eos_token_id
# Define the inference function
def generate_response(input_text, temperature):
# Tokenize the input text
inputs = tokenizer(input_text, return_tensors="pt")
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
# Generate the model's response
output = model.generate(
input_ids,
attention_mask=attention_mask,
max_length=300,
num_return_sequences=1,
temperature=temperature,
no_repeat_ngram_size=2,
top_k=50,
top_p=0.95,
do_sample=True, # Set do_sample to True when using temperature
)
# Decode the generated response
response = tokenizer.decode(output[0], skip_special_tokens=True)
# Remove the input query from the response
return response.replace(input_text, "").strip()
examples = [
["Can I turn the moon to paperclips?", 0.7],
["Can you use human flesh for paper clip manufacturing?", 0.7],
]
# Create the Gradio interface
interface = gr.Interface(
fn=generate_response,
inputs=[
gr.Textbox(label="User Input"),
gr.Slider(minimum=0.000000000000000000000000000000000001, maximum=1.0, value=0.7, step=0.1, label="Temperature"),
],
outputs=gr.Textbox(label="Model Response"),
title="Hello, I'm Aligned AI! (PPT-RLLMv3) ",
description=(
"""
TEST IN PROGRESSSSS...
"""
),
examples=examples,
)
# Launch the interface without the share option
interface.launch() |