🤖 ZyroGod/flan-t5-base-summarization

This repository contains a Merged LoRA Model fine-tuned on the CNN/DailyMail (v3.0.0) dataset for high-quality abstractive text summarization.

• Base Architecture: google/flan-t5-base
• Fine-Tuning Strategy: Parameter-Efficient Fine-Tuning (PEFT LoRA)
• Mixed Precision: Native Bfloat16 (bf16)
• Optimizer: Adafactor
• Evaluation Dataset: CNN/DailyMail (Test split)

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📊 Evaluation Benchmark (CNN/DailyMail Test Set)

Metric	Score
ROUGE-1	0.3881
ROUGE-2	0.1694
ROUGE-L	0.2690
ROUGE-Lsum	0.2690

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🚀 Quick Start & Usage

import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

device = "cuda" if torch.cuda.is_available() else "cpu"
model_name = "ZyroGod/flan-t5-base-summarization"

# Load tokenizer and full model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to(device)
model.eval()

# Prepare input article with prompt prefix
article = (
    "The clock struck midnight, but the old radio kept playing a song that hadn't been "
    "broadcast in decades. From the dark hallway, soft footsteps approached the room. "
    "As the music swelled, a cold hand gently rested on his trembling shoulder."
)
input_text = "summarize: " + article

inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True).to(device)

with torch.no_grad():
    output_ids = model.generate(
        **inputs,
        max_length=128,
        min_length=20,
        num_beams=4,
        length_penalty=2.0,
        no_repeat_ngram_size=3
    )

summary = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print("Summary:", summary)

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⚙️ Hardware & Training Optimization

• Trained on an NVIDIA GeForce RTX 3050 Laptop GPU (4 GB VRAM).
• Gradient Checkpointing enabled (use_reentrant=False).
• Dynamic batch padding with label masking (-100).