Update README.md

README.md

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----
-license: llama3.2
----
+---
+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)