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Will Large Language Models End Programming?

Unite.AI

In areas like image generation diffusion model like Runway ML , DALL-E 3 , shows massive improvements. The post Will Large Language Models End Programming? The rapid advancements in AI, are not limitd to text/code generation. Just see the below tweet by Runway showcasing their latest feature.

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Bridging Large Language Models and Business: LLMops

Unite.AI

LLMOps versus MLOps Machine learning operations (MLOps) has been well-trodden, offering a structured pathway to transition machine learning (ML) models from development to production. The cost of inference further underscores the importance of model compression and distillation techniques to curb computational expenses.

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The Future of Serverless Inference for Large Language Models

Unite.AI

Recent advances in large language models (LLMs) like GPT-4, PaLM have led to transformative capabilities in natural language tasks. Prominent implementations include Amazon SageMaker, Microsoft Azure ML, and open-source options like KServe.

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Supercharging Graph Neural Networks with Large Language Models: The Ultimate Guide

Unite.AI

In parallel, Large Language Models (LLMs) like GPT-4, and LLaMA have taken the world by storm with their incredible natural language understanding and generation capabilities. In this article, we will delve into the latest research at the intersection of graph machine learning and large language models.

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Optimizing Large Language Models (LLMs) on CPUs: Techniques for Enhanced Inference and Efficiency

Marktechpost

Large Language Models (LLMs) built on the Transformer architecture have recently attained important technological milestones. The remarkable skills of these models in comprehending and producing writing that resembles that of a human have had a significant impact on a variety of Artificial Intelligence (AI) applications.

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The Vulnerabilities and Security Threats Facing Large Language Models

Unite.AI

Large language models (LLMs) like GPT-4, DALL-E have captivated the public imagination and demonstrated immense potential across a variety of applications. However, these promising models also pose novel vulnerabilities that must be addressed.

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T-FREE: A Tokenizer-Free Approach for Efficient and Scalable Text Encoding in Large Language Models

Marktechpost

The problem concerns the inefficiencies and limitations of tokenizers used in large language models (LLMs). Furthermore, they often result in large, inefficient vocabularies with many near-duplicate tokens. In conclusion, T-FREE significantly advances text encoding for large language models.