In the field of technological development and innovation, optimizing the performance of Large Language Models (LLMs) is a key research area. This study analyzed models such as GPT-3.5-Turbo, GPT-4, and CodeLlama, which have been evaluated on various algorithmic tasks to measure their accuracy and efficiency.
One of the most important aspects addressed in the research is inference, where vLLM was used to improve performance. A sampling technique with a temperature of 0.0 (greedy decoding) was employed, allowing for accurate responses to be generated in less time. During the experiment, GPT-3.5-Turbo achieved inference times of approximately 30 seconds, while CodeLlama, using A100 GPUs, processed 7B and 13B models in 2 and 5 minutes, respectively.
To evaluate the accuracy of these models, the Exact Match (EM) metric was used, comparing the generated responses with the ground truth labels. The experiments were conducted with datasets containing 250 examples each, focusing on tasks such as object tracking, logical reasoning, temporal sequences, and navigation.
At Q2BSTUDIO, as a company specialized in development and technological services, we focus on the application of these advanced technologies to offer innovative solutions to our clients. Optimizing artificial intelligence models is essential for improving data processing and decision-making in various sectors. Our team of experts constantly works on integrating these advances into tools and platforms that enhance the efficiency and accuracy of intelligent systems.
The analysis of the presented models demonstrates the growing capacity of artificial intelligence to solve complex problems with greater speed and accuracy. At Q2BSTUDIO, we are committed to innovation in this field, exploring new ways to harness the power of AI to develop more efficient and accessible applications for businesses.
