Sumit Gupta

Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri et al. · allen-ai, berkeley

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

Krishnendu Das, Sumit Gupta, Awadhesh Kumar

Clustering is a critical component of decision-making in todays data-driven environments. It has been widely used in a variety of fields such as bioinformatics, social network analysis, and image processing. However, clustering accuracy remains a major challenge in large datasets. This paper presents a comprehensive overview of strategies for selecting the optimal value of k in clustering, with a focus on achieving a balance between clustering precision and computational efficiency in complex data environments. In addition, this paper introduces improvements to clustering techniques for text and image data to provide insights into better computational performance and cluster validity. The proposed approach is based on the Condensed Silhouette method, along with statistical methods such as Local Structures, Gap Statistics, Class Consistency Ratio, and a Cluster Overlap Index CCR and COIbased algorithm to calculate the best value of k for K-Means clustering. The results of comparative experiments show that the proposed approach achieves up to 99 percent faster execution times on high-dimensional datasets while retaining both precision and scalability, making it highly suitable for real time clustering needs or scenarios demanding efficient clustering with minimal resource utilization.