AI News
JANUARY 20, 2025
This additional pre-training step enhances the models reasoning capabilities and resolves many of the limitations noted in DeepSeek-R1-Zero. Notably, DeepSeek-R1 achieves performance comparable to OpenAI’s much-lauded o1 system across mathematics, coding, and general reasoning tasks, cementing its place as a leading competitor.
Analytics Vidhya
JANUARY 29, 2025
The Chinese AI model is the recent advancements in reinforcement learning (RL) with large language models (LLMs) that have led to the development of Kimi k1.5, a model that promises to reshape the landscape of generative AI reasoning. Outshines OpenAI o1 appeared first on Analytics Vidhya.
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Analytics Vidhya
FEBRUARY 22, 2025
Fine-tuning large language models (LLMs) is essential for optimizing their performance in specific tasks. OpenAI provides a robust framework for fine-tuning GPT models, allowing organizations to tailor AI behavior based on domain-specific requirements.
AssemblyAI
MARCH 7, 2023
In recent years, significant efforts have been put into scaling LMs into Large Language Models (LLMs). In this article, we'll explore the concept of emergence as a whole before exploring it with respect to Large Language Models. What is the cause of these emergent abilities, and what do they mean?
Analytics Vidhya
JANUARY 7, 2025
Fine-tuning large language models (LLMs) is an essential technique for customizing LLMs for specific needs, such as adopting a particular writing style or focusing on a specific domain. OpenAI and Google AI Studio are two major platforms offering tools for this purpose, each with distinct features and workflows.
Analytics Vidhya
JANUARY 29, 2024
Introduction Large Language Models (LLMs) have captivated the world with their ability to generate human-quality text, translate languages, summarize content, and answer complex questions. Prominent examples include OpenAI’s GPT-3.5, Google’s Gemini, Meta’s Llama2, etc.
AI News
NOVEMBER 28, 2024
OpenAI and other leading AI companies are developing new training techniques to overcome limitations of current methods. Addressing unexpected delays and complications in the development of larger, more powerful language models, these fresh techniques focus on human-like behaviour to teach algorithms to ‘think.
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