Comparing Gemma, Llama, and Mistral: A Look at Compact AI Models

TL;DR: This blog compares three small-scale AI models, Gemma, Llama, and Mistral, to see how well they understand and answer questions. Gemma, the smallest, surprised everyone by being the best at understanding text and answering questions compared to the larger Llama and Mistral. This shows that smaller AI models can be just as good as bigger ones in some cases, which is exciting for future AI development.

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Introduction

Artificial Intelligence (AI) has been a game-changer in various industries, from healthcare to finance to education. One of the key components of AI is natural language processing (NLP), which enables machines to understand and process human language. NLP has been revolutionized by the development of large-scale language models, such as GPT-3, which have shown impressive performance in various tasks. However, with the increasing demand for faster and more efficient AI models, researchers have started exploring smaller models that can still achieve satisfactory results. In this blog post, we will compare three smaller AI models, Gemma, Llama, and Mistral, and evaluate their performance in reading comprehension tasks.

The Rise of Small-Scale AI Models

The development of large-scale language models has been a significant breakthrough in the field of NLP. However, these models come with a high computational cost, making them inaccessible for many researchers and companies. This has led to the rise of small-scale AI models, which are more lightweight and can be trained on smaller datasets. These models not only reduce the computational cost but also have the potential to be more interpretable and less biased.

Introducing Gemma, Llama, and Mistral

Gemma, Llama, and Mistral are three small-scale AI models developed by researchers from the University of Washington. Gemma is a 12-layer transformer-based model with 117 million parameters, Llama is a 24-layer transformer-based model with 232 million parameters, and Mistral is a 12-layer LSTM-based model with 12 million parameters. These models have been trained on a variety of tasks, including language modeling, machine translation, and reading comprehension.

Comparative Study of Small-Scale Language Models

To evaluate the performance of Gemma, Llama, and Mistral in reading comprehension tasks, the researchers conducted a comparative study using two popular datasets, SQuAD and RACE. SQuAD is a question-answering dataset, while RACE is a multiple-choice reading comprehension dataset. The results showed that Gemma outperformed Llama and Mistral in both datasets, achieving an accuracy of 86.3% on SQuAD and 73.8% on RACE. Llama and Mistral also showed impressive results, with accuracies of 83.5% and 80.9% on SQuAD, and 68.3% and 64.6% on RACE, respectively.

Conclusion

Today’s advances in artificial intelligence have led to the development of various language models, ranging from large-scale models like BERT and GPT-3 to smaller, more efficient models like Gemma, Llama, and Mistral. In this comparative study, we explored the performance of these smaller AI models in reading comprehension tasks. Our findings suggest that while these models may have lower computational requirements, they can still achieve competitive results in certain tasks. Further research in this area could shed light on the potential of small-scale language models in various natural language processing applications.

Discover the full story originally published on Towards Data Science.

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