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---
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)
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