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The following text has been translated from Korean to English using AssistAce . Sungjin (James) Kim, Ph.D. | LinkedIn  | YouTube Case Study of Translation LLM Fine-Tuning Introduction The technology of Large Language Models (LLMs) is advancing rapidly. There are various ways to utilize LLMs, including prompting, embedding, and fine-tuning. In this article, we will focus on fine-tuning, which requires a significant amount of GPU computing resources. While it is possible to sec

Sungjin (James) Kim, Ph.D. | LinkedIn Table of Contents Introduction The Structure and Operating Principle of the Transformer Encoder Operation Decoder Operation Types and Applications of Transformer-based LLMs Encoder-Only Approach Decoder-Only Approach Encoder-Decoder Approach Latest Methods Implications Introduction Generative AI (GenAI), including Large Language Models (LLMs), has seen significant advancements since the introduction of the transformer architecture[1]. Int

Status In the AI realm, the GPU landscape is starkly divided. The technological landscape is increasingly being divided into two distinct categories: 'GPU rich' and 'GPU poor'. This division, predominantly seen in the AI industry, is creating a significant gap in capabilities and advancements between different companies and regions. This gap is primarily due to the unequal distribution and accessibility of Graphics Processing Units (GPUs). Giants like Google, Microsoft, and M

BACKGROUND The customer was looking to start an LLM project but was in a time crunch. The current solution was to purchase Nvidia’s DGX...