AventIQ-AI / pythia-410m

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

• 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, 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
└── 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.