children_educational_summarizer

import torch
from transformers import AutoTokenizer, BartForConditionalGeneration
from peft import PeftModel

model_id = "SeifElden2342532/children_educational_summarizer"

tokenizer = AutoTokenizer.from_pretrained(model_id)

base  = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn").to("cuda")
model = PeftModel.from_pretrained(base, model_id)
model = model.merge_and_unload()
model.eval()

text = "your lesson text here..."

inputs = tokenizer(
    text,
    max_length=1024,
    truncation=True,
    padding="max_length",
    return_tensors="pt",
).to("cuda")

with torch.no_grad():
    summary_ids = model.generate(
        input_ids      = inputs["input_ids"],
        attention_mask = inputs["attention_mask"],
        num_beams      = 4,
        max_length     = 256,
        early_stopping = True,
    )

summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(summary)