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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Model i tokenizer
model_name = "sshleifer/tiny-gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float32, # ważne dla CPU
)
# Funkcja do generowania odpowiedzi
def generate_text(prompt):
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
output = model.generate(
**inputs,
max_new_tokens=150,
do_sample=True,
top_p=0.9,
temperature=0.7,
pad_token_id=tokenizer.eos_token_id,
)
return tokenizer.decode(output[0], skip_special_tokens=True)
# Interfejs Gradio
gr.Interface(
fn=generate_text,
inputs=gr.Textbox(lines=5, label="Wprowadź tekst (prompt)"),
outputs=gr.Textbox(label="Wygenerowany tekst"),
title="sshleifer/tiny-gpt2 – Generowanie tekstu po polsku",
description="Testowanie sshleifer/tiny-gpt2 -> Uruchamiany na CPU.",
).launch()
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