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Understanding Fine-Tuning of Large Language Models: A Comprehensive Overview
Fine-tuning Large Language Models (LLMs) is crucial for customizing AI to meet specific business needs. This blog delves into the two primary types of fine-tuning: instruction tuning, which enhances a model's ability to follow complex commands, and alignment tuning, which ensures outputs align with human values. By understanding these processes, businesses can effectively leverage AI for customer support, content creation, and more.
![[Prompt Engineering - Part 2] The Essence of Prompt Engineering: A Comprehensive Guide to Maximizing LLM Usage](https://images.squarespace-cdn.com/content/v1/659384103b38c97cdaf368bd/1723078816174-ZQL4GH8K5FDO8D0MFUHT/Prompt+Engineering+Guide+%281%29.jpg)
[Prompt Engineering - Part 2] The Essence of Prompt Engineering: A Comprehensive Guide to Maximizing LLM Usage
Delve into the significance of Prompt Engineering and explore both fundamental and advanced Prompt Engineering techniques. Follow our comprehensive guide to maximizing the potential of Large Language Models.
Empowering Vietnam's AI Potential: A Recap of Upstage's Event and Interview Content with Vietnam Experts
Explore Vietnam's AI scene through Upstage event recaps and interviews with Vietnamese experts. Discover more about our efforts in taking new steps towards global expansion.
![[Prompt Engineering - Part 1] Maximizing the Use of LLM with Prompt Design](https://images.squarespace-cdn.com/content/v1/659384103b38c97cdaf368bd/52c31365-df8e-48f8-8dcb-517640735b81/Prompt+Engineering+Guide.jpg)
[Prompt Engineering - Part 1] Maximizing the Use of LLM with Prompt Design
To fully leverage the power of LLMs, crafting effective prompts is imperative. In this blog, we will explore the concept of Prompt Engineering using SolarLLM and examine methodologies to elicit optimal results from Generative AI.
SAAS - Solving Ability Amplification Strategy for Enhanced Mathematical Reasoning in Large Language Models
Introducing "SAAS: Solving Ability Amplification Strategy for Enhanced Mathematical Reasoning in Large Language Models," a groundbreaking research paper by Upstage AI, Mathpresso Inc., and KT Corp. This novel approach leverages sequential learning, combining Chain-of-Thought (CoT) and Program-of-Thought (PoT) methodologies to significantly boost the mathematical reasoning and problem-solving skills of Large Language Models (LLMs). Our findings show that SAAS achieves state-of-the-art performance on benchmarks like GSM8K and MATH, outperforming larger models and setting new standards in AI-driven mathematical reasoning. Discover how SAAS can elevate your LLM’s capabilities.
GPT Series and Development Process
Dive into the five-year odyssey of OpenAI's GPT series, tracing its transformation from a foundational language model to a refined recurrent neural network.