Marktechpost

APRIL 16, 2024

The book covers the inner workings of LLMs and provides sample codes for working with models like GPT-4, BERT, T5, LLaMA, etc. It explains the fundamentals of LLMs and generative AI and also covers prompt engineering to improve performance. The book covers topics like Auto-SQL, NER, RAG, Autonomous AI agents, and others.

Large Language Models 96

Large Language Models Prompt Engineer Prompt Engineering Auto-complete 96

Marktechpost

JULY 11, 2023

The best example is OpenAI’s ChatGPT, the well-known chatbot that does everything from content generation and code completion to question answering, just like a human. Even OpenAI’s DALL-E and Google’s BERT have contributed to making significant advances in recent times. What is AutoGPT? What is BabyAGI?

Auto-complete 40

Auto-complete Generative AI Large Language Models OpenAI 40

deepsense.ai

OCTOBER 8, 2023

We compare the existing solutions and explain how they work behind the scenes. General purpose coding agents Auto-GPT Auto-GPT was one of the first AI agents using Large Language Models to make waves, mainly due to its ability to independently handle diverse tasks. It can be augmented or replaced by human feedback.

Auto-complete 92

Auto-complete LLM AI AI 92

TheSequence

MAY 24, 2023

This is also where I met Lewis Tunstall and as language models with BERT and GPT-2 started taking off we decided to start working on a textbook about transformer models and the Hugging Face ecosystem. Could you explain the data curation and training process required for building such a model? data or auto-generated files).

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Auto-complete ML Python Machine Learning 59

AWS Machine Learning Blog

DECEMBER 4, 2023

In this post, we explain how we built an end-to-end product category prediction pipeline to help commercial teams by using Amazon SageMaker and AWS Batch , reducing model training duration by 90%. An important aspect of our strategy has been the use of SageMaker and AWS Batch to refine pre-trained BERT models for seven different languages.

BERT 94

BERT Auto-complete Data Scientist Machine Learning 94

Jay Alammar

DECEMBER 16, 2020

This article focuses on auto-regressive models, but these methods are applicable to other architectures and tasks as well. input saliency is a method that explains individual predictions. This exposition series continues the pursuit to interpret and visualize the inner-workings of transformer-based language models.

Explainability 52

Explainability Auto-complete Auto-classification Neural Network 52

Unite.AI

AUGUST 1, 2023

In zero-shot learning, no examples of task completion are provided in the model. Chain-of-thought Prompting Chain-of-thought prompting leverages the inherent auto-regressive properties of large language models (LLMs), which excel at predicting the next word in a given sequence.

Prompt Engineer 246

Prompt Engineer Prompt Engineering ChatGPT Convolutional Neural Networks 246