A Comparative Analysis of ChatGPT and Open-source Models

Avatar for Prakash Dagwal
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A Comparative Analysis of AI Models

The field of artificial intelligence (AI) is ever-evolving with remarkable advancements. With GPT-4’s impressive 1.7 trillion parameters and the positive response to ChatGPT, a crucial question emerges: does a higher parameter count always mean a better model? The answer depends on the specific AI application. Striking the right balance between the number of parameters and model performance is essential in finding the most suitable AI text-generative model. In this article, we will explore how parameter count impacts AI models’ performance and shapes the future of text generation.

Consider this: Increasing parameters requires more computational resources and drives up costs.

To discover the optimal balance between cost and performance for the Icecat Text Generative AI model, we explored ChatGPT, the Open-source framework TensorFlow, and two pre-trained open-source models: Falcon 40B and Falcon 7B. 

Assumption: the TensorFlow model is ideal for building new AI models from scratch. Therefore, the resulting new model will likely have significantly fewer parameters than other pre-trained models.

Comparative Analysis

Below, you can find the outcome of our comparative analysis.

Comparative Analysis

Marketing Text Comparisons

Below are examples of AI-generated marketing texts using competing models.

Marketing Text Comparisions

Style Guided Bullet Point Comparisons

Below are examples of AI-generated bullet points by competing AI models.

Style Guided Bullet Point
Style Guided Bullet Point
Style Guided Bullet Point

Conclusion

More parameters lead to better responses but come at a high cost. We can fine-tune lower parameter models for specific needs, making them cost-efficient. For Icecat, we seek a suitable open-source model that can be trained and fine-tuned with Icecat data to cost-effectively achieve our text generation goal.

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