Flipboard

MARCH 23, 2023

Maybe it’s surprising that ChatGPT can write software, maybe it isn’t; we’ve had over a year to get used to GitHub Copilot, which was based on an earlier version of GPT. It’s a convenient user interface built around one specific language model, GPT-3.5, which has received some specialized training. with specialized training.

ChatGPT 133

ChatGPT Large Language Models OpenAI Explainability 133

ODSC - Open Data Science

FEBRUARY 14, 2023

It is the latest in the research lab’s lineage of large language models using Generative Pre-trained Transformer (GPT) technology. Trained with 570 GB of data from books and all the written text on the internet, ChatGPT is an impressive example of the training that goes into the creation of conversational AI.

ChatGPT 124

ChatGPT Large Language Models OpenAI Conversational AI 124

deepsense.ai

FEBRUARY 28, 2024

The wide adoption of ChatGPT and other large language models (LLMs) among individuals made companies of all sizes and across all sectors of industry wonder how they could benefit from this upward-trending technology. The two main topics we will dive into are quantized inference and parameter-efficient fine-tuning.

Generative AI 52

Generative AI LLM Neural Network Algorithm 52

AWS Machine Learning Blog

APRIL 18, 2023

Large language models (LLMs) with billions of parameters are currently at the forefront of natural language processing (NLP). In order to train an LLM to become an expert in a particular domain, fine-tuning is usually required.

Large Language Models 65

Large Language Models LLM NLP Deep Learning 65

Heartbeat

JANUARY 18, 2024

Many advancements have been made since ChatGPT, including open-source and licensed models. An open-source model that is just as good as GPT 3.5 or even GPT 4? Replicate is a cloud platform that hosts large machine learning models for easy deployment. Compared to the popular closed-source model, GPT-3.5,

LLM 52

LLM Chatbots Large Language Models Python 52

AWS Machine Learning Blog

APRIL 18, 2023

Large language models (LLMs) with billions of parameters are currently at the forefront of natural language processing (NLP). In order to train an LLM to become an expert in a particular domain, fine-tuning is usually required.

LLM 52

LLM NLP Deep Learning ML 52

AWS Machine Learning Blog

JULY 24, 2023

This blog post was co-authored, and includes an introduction, by Zilong Bai, senior natural language processing engineer at Patsnap. They use big data (such as a history of past search queries) to provide many powerful yet easy-to-use patent tools. Patsnap had trained a customized GPT-2 model for such a purpose.

Metadata 65

Metadata Generative AI Natural Language Processing Deep Learning 65

Bugra Akyildiz

JUNE 18, 2023

Articles Stanford wrote a blog post for a new framework Levanter , a new JAX -based codebase for training foundation models. Scalable : Levanter is designed to scale to large models, and to be able to train on a variety of hardware, including GPUs and TPUs. This model is 4 points behind LLaMA-7B, and 1.3

LLM 52

LLM Large Language Models Deep Learning ETL 52

Bugra Akyildiz

DECEMBER 3, 2022

I am sure this is relatively cherry-picked example, but it shows the training methodology and reinforcement learning’s success on a very large language model well. The model is often excessively verbose and overuses certain phrases, such as restating that it’s a language model trained by OpenAI.

OpenAI 52

OpenAI ChatGPT Data Drift Robotics 52

AWS Machine Learning Blog

AUGUST 7, 2023

The video gaming industry has an estimated user base of over 3 billion worldwide 1. It will explain the thought process and experimentation behind the solution, including the model training and development process. The customer had both cost and time constraints that made this solution unviable.

Large Language Models 73

Large Language Models LLM BERT ML 73

The MLOps Blog

AUGUST 11, 2023

There comes a time when every ML practitioner realizes that training a model in Jupyter Notebook is just one small part of the entire project. How should they be implemented to accommodate scalability and adaptability whilst maintaining an infrastructure that’s easy to troubleshoot? 1 Data Ingestion (e.g., 1 Data Ingestion (e.g.,

ML 52

ML Machine Learning Data Ingestion Deep Learning 52

AWS Machine Learning Blog

MAY 31, 2023

In 2018, BERT-large made its debut with its 340 million parameters and innovative transformer architecture, setting the benchmark for performance on NLP tasks. Recent advances in ML have given rise to a new class of models known as foundation models , which have billions of parameters and are trained on massive amounts of data.

Automation 66

Automation Python Prompt Engineer Prompt Engineering 66

The MLOps Blog

FEBRUARY 28, 2023

In this era of large language models (LLMs), monolithic foundation models, and increasingly enormous datasets, distributed training is a must, as both data and model weights very rarely fit on a single machine. This article will touch on ten of the most common errors in distributed model training and will suggest solutions to each of them.

Metadata 69

Metadata Algorithm Large Language Models Deep Learning 69