nlp

Eden AI provides a unique API connected to the best AI engines Link Github

We all love chat.openai.com, but… It’s TERRIBLY laggy, has daily limits, and is only accessible through an archaic web interface. This repo is ChatGPT re-created with GPT-3.5 LLM as Telegram Bot. And it works great. Link

Say ‘goodbye’ to the blank page for good Link

In this guide, we discuss how to fine-tune GPT-3 to create a factual question-and-answer bot based on additional knowledge. Link

Generative AI sparked several “wow” moments in 2022. From generative art tools like OpenAI’s DALL-E 2, Midjourney, and Stable Diffusion, to the next generation of Large Language Models like OpenAI’s GPT-3.5 generation models, BLOOM, and chatbots like LaMDA and ChatGPT. Link

LangChain is a popular framework that allows users to quickly build apps and pipelines around Large Language Models. It integrates directly with OpenAI’s GPT-3 and GPT-3.5 models and Hugging Face’s open-source alternatives like Google’s flan-t5 models. Link

We’re excited to announce our new service offering: GPT-3 fine tuning as a service. If you’re looking to achieve better results, reduce latency, and save costs on a wide range of natural language processing (NLP) tasks, we’re here to help. Link

If you’ve spent any time with GPT-3 or ChatGPT, you’ve likely thought about how useful it would be if you could point them at a specific, current collection of text or documentation and have it use that as part of its input for answering questions. Link

LIGHTNING TRANSFORMERS documentation Link

Link Doc examples Doc quickstart Natural Language Processing (NLP) for Semantic Search

Many use cases require GPT-3 to respond to user questions with insightful answers. For example, a customer support chatbot may need to provide answers to common questions. The GPT models have picked up a lot of general knowledge in training, but we often need to ingest and use a large library of more specific information. Link Examples

Quickchat is a human-like AI assistant that provides accurate and instant answers to customer questions. Link

The Large Language Model revolution started with the advent of transformers in 2017. Since then there has been an exponential growth in the models trained. Models with 100B+ parameters have been trained. These pre-trained models have changed the way NLP is done. It is much easier to pick a pre-trained model and fine-tune it for a downstream task ( sentiment, question answering, entity recognition etc.. ) than training a model from scratch.

Fine tuning NLP models (GPT-3/ChatGPT) Link Fine-Tuning GPT-3/ChatGPT and Zapier Integration: A Tutorial for No Code OpenAI Developers

An unofficial PyTorch implementation of VALL-E, based on the EnCodec tokenizer. Link