"You are an assistant that provides insightful analysis and solutions for AWS MWAA error logs. Your explanations should cover Error Identification, Context, Cause, Impact, Solution, and Prevention, and should be easy to understand for those not deeply familiar with AWS Services."
This article discusses the process of training a large language model (LLM) using reinforcement learning from human feedback (RLHF) and a new alternative method called Direct Preference Optimization (DPO). The article explains how these methods help align the LLM with human expectations and make it more efficient.
This is an open source effort to create a similar experience to OpenAI's GPTs and Assistants API. It builds upon LangChain, LangServe and LangSmith. OpenGPTs gives you more control
Kresmo is an Arduino sketch that uses an OpenAI-compatible API to generate a random and brief pithy saying. The sketch uses the U8g2 library for displaying text on an OLED screen, and the WiFi library for connecting to the internet. The ESP32-C3-0.42 module combines all this hardware into one tiny board.
Models referred to as "GPT 3.5"
GPT-3.5 series is a series of models that was trained on a blend of text and code from before Q4 2021. The following models are in the GPT-3.5 series:
code-davinci-002 is a base model, so good for pure code-completion tasks
text-davinci-002 is an InstructGPT model based on code-davinci-002
text-davinci-003 is an improvement on text-davinci-002
gpt-3.5-turbo-0301 is an improvement on text-davinci-003, optimized for chat
GPT-3.5 series is a series of models that was trained on a blend of text and code from before Q4 2021. The following models are in the GPT-3.5 series:
code-davinci-002 is a base model, so good for pure code-completion tasks
text-davinci-002 is an InstructGPT model based on code-davinci-002
text-davinci-003 is an improvement on text-davinci-002
gpt-3.5-turbo-0301 is an improvement on text-davinci-003, optimized for chat
