---
license: llama3.2
base_model: meta-llama/Meta-Llama-3-3B
tags:
- summarization
- news
- llama3
- fine-tuned
- llama3.2
---

# 📰 Automatic News Summarizer (Fine-tuned on LLaMA 3.2 3B)

This model is a fine-tuned version of **Meta-LLaMA-3-3B**, optimized for **automatic news summarization** tasks. It is designed to generate concise and coherent summaries of news articles using state-of-the-art language modeling techniques.

## 📌 Model Details

- **Base model**: [Meta-LLaMA-3-3B](https://huggingface.co/meta-llama/Meta-Llama-3-3B)
- **Fine-tuned for**: Summarization
- **Architecture**: Decoder-only Transformer (LLaMA 3.2 3B)
- **License**: [Meta LLaMA 3 Community License](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)

## 📚 Training

- Fine-tuned on a dataset of curated news articles and summaries
- Optimized for relevance, coherence, and brevity
- Training framework: Hugging Face Transformers
- Fine-tuning platform: Google Colab

## 🚀 Usage Example

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "punit16/automatic_news_summarizer"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "The government has announced a new policy today aimed at reducing air pollution in major cities..."

inputs = tokenizer(input_text, return_tensors="pt")
summary_ids = model.generate(**inputs, max_new_tokens=512)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)

print("Summary:", summary)