| 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. | |
| Model Details | |
| ------------- | |
| * **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, AutoModelForCausalLM | |
| tokenizer = AutoTokenizer.from_pretrained("AventIQ-AI/pythia-410m") | |
| model = AutoModelForCausalLM.from_pretrained(model_name, low_cpu_mem_usage=True) | |
| # Example usage | |
| text = "This product is amazing!" | |
| inputs = tokenizer(text, return_tensors="pt") | |
| outputs = model.generate(**inputs, max_new_tokens=20) | |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) | |
| 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 | |
| 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. |