Marktechpost

MAY 12, 2024

However, these models pose challenges, including computational complexity and GPU memory usage. Despite great success in various applications, there is an urgent need to find a cost-effective way to serve these models. Still, an increase in model size and generation length leads to an increase in memory usage of the KV cache.

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LLM Auto-complete Large Language Models BERT 120

John Snow Labs

JUNE 27, 2023

Prepare to be amazed as we delve into the world of Large Language Models (LLMs) – the driving force behind NLP’s remarkable progress. In this comprehensive overview, we will explore the definition, significance, and real-world applications of these game-changing models. What are Large Language Models (LLMs)?

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AWS Machine Learning Blog

MARCH 6, 2023

Language models are statistical methods predicting the succession of tokens in sequences, using natural text. Large language models (LLMs) are neural network-based language models with hundreds of millions ( BERT ) to over a trillion parameters ( MiCS ), and whose size makes single-GPU training impractical.

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Unite.AI

AUGUST 1, 2023

The spotlight is also on DALL-E, an AI model that crafts images from textual inputs. One such model that has garnered considerable attention is OpenAI's ChatGPT , a shining exemplar in the realm of Large Language Models. In zero-shot learning, no examples of task completion are provided in the model.

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Marktechpost

APRIL 16, 2024

Quick Start Guide to Large Language Models This book guides how to work with, integrate, and deploy LLMs to solve real-world problems. The book covers the inner workings of LLMs and provides sample codes for working with models like GPT-4, BERT, T5, LLaMA, etc.

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Large Language Models Auto-complete Prompt Engineer Prompt Engineering 104

IBM Journey to AI blog

NOVEMBER 24, 2023

Generating configuration management inputs (for CMDB)and changing management inputs based on release notes generated from Agility tool work items completed per release are key Generative AI leverage areas. The ability to generate insights for security validation (from application and platform logs, design points, IAC, etc.)

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Towards AI

JULY 13, 2024

Now, imagine a large, pretrained model tailored for time-series data — one that delivers accurate predictions without extensive retraining. Their decoder-only model, inspired by NLP giants like BERT, uses a patch-based approach to handle data efficiently. However, there is a trade-off.

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Auto-complete Neural Network BERT Large Language Models 105

deepsense.ai

OCTOBER 8, 2023

Introduction While the concept of AI agents has been around for decades, it is undeniable that recent advancements in Large Language Models (LLMs) have revolutionized this field, opening up a whole new realm of possibilities and applications. Cons of Auto-GPT Operating within a loop – the costs can rack up quickly!

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DataRobot Blog

FEBRUARY 2, 2023

Large language models, also known as foundation models, have gained significant traction in the field of machine learning. These models are pre-trained on large datasets, which allows them to perform well on a variety of tasks without requiring as much training data. What Are Large Language Models?

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BERT Large Language Models Natural Language Processing Machine Learning 52

Marktechpost

JULY 11, 2023

The major reason for the exponentially increasing popularity is the development of Large Language Models. LLMs, the Artificial Intelligence models that are designed to process natural language and generate human-like responses, are trending. What is AutoGPT? Unlike the previous version, GPT 3.5, What is BabyAGI?

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Auto-complete Generative AI Large Language Models OpenAI 40

AWS Machine Learning Blog

APRIL 17, 2023

Quantization and compression can reduce model size and serving cost by reducing the precision of weights or reducing the number of parameters via pruning or distillation. Compilation can optimize the computation graph and fuse operators to reduce memory and compute requirements of a model.

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Prompt Engineer Prompt Engineering Deep Learning Machine Learning 103

Snorkel AI

MARCH 2, 2023

Today we’re going to be talking essentially about how responsible generative-AI-model adoption can happen at the enterprise level, and what are some of the promises and compromises we face. The foundation of large language models started quite some time ago. What are the promises? Billions of parameters.

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Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Snorkel AI

MARCH 2, 2023

Today we’re going to be talking essentially about how responsible generative-AI-model adoption can happen at the enterprise level, and what are some of the promises and compromises we face. The foundation of large language models started quite some time ago. What are the promises? Billions of parameters.

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Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

The MLOps Blog

SEPTEMBER 26, 2024

This “making up” event is what we call a hallucination, a term popularized by Andrej Karpathy in 2015 in the context of RNNs and extensively used nowadays for large language models (LLMs). Self-attention is the mechanism where tokens interact with each other (auto-regressive) and with the knowledge acquired during pre-training.

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AWS Machine Learning Blog

NOVEMBER 1, 2023

Visual language processing (VLP) is at the forefront of generative AI, driving advancements in multimodal learning that encompasses language intelligence, vision understanding, and processing. Additionally, you benefit from advanced features like auto scaling of inference endpoints, enhanced security, and built-in model monitoring.

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Auto-classification LLM Auto-complete Generative AI 121

Topbots

JUNE 29, 2023

In this article, we will consider the different implementation aspects of Text2SQL and focus on modern approaches with the use of Large Language Models (LLMs), which achieve the best performance as of now (cf. [2]; 3] provides a more complete survey of Text2SQL data augmentation techniques.

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AWS Machine Learning Blog

DECEMBER 11, 2024

Two open-source libraries, Ragas (a library for RAG evaluation) and Auto-Instruct, used Amazon Bedrock to power a framework that evaluates and improves upon RAG. Generating improved instructions for each question-and-answer pair using an automatic prompt engineering technique based on the Auto-Instruct Repository.

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Generative AI Chatbots Prompt Engineer Prompt Engineering 80

AWS Machine Learning Blog

OCTOBER 23, 2024

It is a family of embedding models with a BERT-like architecture, designed to produce high-quality embeddings from text data. The BGE models come in three sizes: bge-large-en-v1.5: Deploy the model to SageMaker. Auto scaling helps make sure the endpoint can handle varying workloads efficiently.

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