Create README.md

README.md

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+# News Article Generation with GPT2
+
+This repository hosts a quantized version of the GPT2 model, fine-tuned for generation of news article tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments.
+
+## Model Details
+
+- **Model Architecture:** gpt2
+- **Task:** Text Summarization  
+- **Dataset:** Hugging Face's `ag_news'  
+- **Quantization:** Float16  
+- **Fine-tuning Framework:** Hugging Face Transformers  
+
+## Usage
+
+### Installation
+
+```sh
+pip install transformers torch
+```
+
+### Loading the Model
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model_name = "AventIQ-AI/gpt2-news-article-generation"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
+
+import torch
+import html
+
+# Define test text
+test_text = "The future of AI"
+
+# Tokenize input
+inputs = tokenizer(test_text, return_tensors="pt").to("cuda")
+
+# Generate response
+with torch.no_grad():
+    output_tokens = model.generate(
+        **inputs,
+        max_length=200,  # Allow longer responses
+        num_beams=5,  # Balances quality & diversity
+        repetition_penalty=2.0,  # Reduce repeating patterns
+        temperature=0.2,  # More deterministic response
+        top_k=100,  # Allows more diverse words
+        top_p=0.9,  # Keeps probability confidence
+        do_sample=True,  # Sampling for variety
+        no_repeat_ngram_size=3,  # Prevents excessive repetition
+        num_return_sequences=1,  # Returns one best sequence
+        early_stopping=True,  # Stops when response is complete
+        length_penalty=1.2,  # Balances response length
+        pad_token_id=tokenizer.eos_token_id,  # Prevents truncation
+        eos_token_id=tokenizer.eos_token_id,  # Ensures completion
+        return_dict_in_generate=True,  # Structured output
+        output_scores=True  # Debugging purposes
+    )
+
+# Decode and clean response
+generated_response = tokenizer.decode(output_tokens.sequences[0], skip_special_tokens=True)
+cleaned_response = html.unescape(generated_response).replace("#39;", "'").replace("quot;", '"')
+
+print("\nGenerated Response:\n", cleaned_response)
+```
+
+# 📊 ROUGE Evaluation Results
+ 
+After fine-tuning the **T5-Small** model for text summarization, we obtained the following **ROUGE** scores:
+
+| **Metric**  | **Score**  | **Meaning** |
+|-------------|-----------|-------------|
+| **ROUGE-1** | **0.3061** (~30%) | Measures overlap of **unigrams (single words)** between the reference and generated summary. |
+| **ROUGE-2** | **0.1241** (~12%) | Measures overlap of **bigrams (two-word phrases)**, indicating coherence and fluency. |
+| **ROUGE-L** | **0.2233** (~22%) | Measures **longest matching word sequences**, testing sentence structure preservation. |
+| **ROUGE-Lsum** | **0.2620** (~26%) | Similar to ROUGE-L but optimized for summarization tasks. |
+ 
+
+## Fine-Tuning Details
+
+### Dataset
+
+The Hugging Face's `ag_news` dataset was used, containing the text and their labels.
+
+### Training
+
+- Number of epochs: 3
+- Batch size: 4  
+- Evaluation strategy: epoch  
+- Learning rate: 5e-5  
+
+### Quantization
+
+Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency.
+
+## Repository Structure
+
+```
+.
+├── model/               # Contains the quantized model files
+├── tokenizer_config/    # Tokenizer configuration and vocabulary files
+├── model.safetensors/   # Quantized Model
+├── README.md            # Model documentation
+```
+
+## Limitations
+
+- The model may not generalize well to domains outside the fine-tuning dataset.  
+- Quantization may result in minor accuracy degradation compared to full-precision models.  
+
+## Contributing
+
+Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.
+