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README.md

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 Pythia Quantized Model for Sentiment Analysis
-This repository hosts a quantized version of the Pythia model, fine-tuned for sentiment analysis tasks. 
-The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable
-for resource-constrained environments.
+=============================================
+This repository hosts a quantized version of the Pythia model, fine-tuned for sentiment analysis tasks.
+The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for 
+resource-constrained environments.
 
 Model Details
-Model Architecture: Pythia
-Task: Sentiment Analysis
-Dataset: Twitter Tweets 
-Quantization: FP16
+-------------
 
-Fine-tuning Framework: Hugging Face Transformers
-The quantized model achieves comparable performance to its full-precision counterpart while significantly reducing memory usage and inference latency.
+*   **Developed By:** AventIQ-AI
+*   **Model Architecture:** Pythia-410m
+*   **Task:** Sentiment Analysis
+*   **Dataset:** IMDb Reviews
+*   **Quantization:** Float16
+*   **Fine-tuning Framework:** Hugging Face Transformers
+
+The quantized model achieves comparable performance to the full-precision model while reducing memory usage and inference time.
+
+Usage
+-----
+
+### Installation
+
+    pip install transformers torch
+
+### Loading the Model
+
+    from transformers import AutoTokenizer, AutoModelForSequenceClassification
+    
+    tokenizer = AutoTokenizer.from_pretrained("AventIQ-AI/pythia-410m")
+    model = AutoModelForSequenceClassification.from_pretrained("AventIQ-AI/pythia-410m")
+    
+    # Example usage
+    text = "This product is amazing!"
+    inputs = tokenizer(text, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = logits.argmax().item()
+    print("Predicted class:", predicted_class)
+
+Performance Metrics
+-------------------
+
+*   **Accuracy:** 0.56
+*   **F1 Score:** 0.56
+*   **Precision:** 0.68
+*   **Recall:** 0.56
+
+Fine-Tuning Details
+-------------------
+
+### Dataset
+
+The IMDb Reviews dataset was used, containing both positive and negative sentiment examples.
+
+### Training
+
+*   Number of epochs: 3
+*   Batch size: 8
+*   evaluation_strategy= epoch
+*   Learning rate: 2e-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/           # Tokenizer configuration and vocabulary files
+    ├── model.safensors/            # Fine Tuned Model
+    ├── README.md            # Model documentation
+    └── LICENSE              # License for the repository
+
+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.
+
+
+License
+-------
+
+This project is licensed under the Apache License 2.0. See the LICENSE file for more details.
+
+Contributing
+------------
+
+Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.