2024年12月5日 · ProprietaryDatafromDataPartnerships: Tofurtherenhancethecapabilitiesofo1and o1-mini,weformedpartnershipstoaccesshigh-valuenon-publicdatasets. Theseproprietary
In this paper, we explore a semi-supervised approach for language understanding tasks using a combination of unsupervised pre-training and supervised fine-tuning. Our goal is to learn a universal representation that transfers with little adaptation to a …
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This report outlines the safety work carried out for the OpenAI o1-preview and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.
[2303.08774] GPT-4 Technical Report - arXiv.org
2023年3月15日 · View a PDF of the paper titled GPT-4 Technical Report, by OpenAI and 279 other authors View PDF HTML (experimental) Abstract: We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
How can I process a pdf using OpenAI's APIs (GPTs)?
2023年11月12日 · Open a pdf on say something like word or WPS, try printing pdf to image. These images can be given to the vision api. Now do the task programmatically. Use some library to convert pdf to images. Or extract the images from pdf and only use those. Both are options. I wrote this guide as a demo: typescript.tv/hands-on/…
Evaluation of OpenAI o1: Opportunities and Challenges of AGI
2024年9月27日 · View PDF Abstract: This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguistics, and social sciences.
Welcome to Spinning Up in Deep RL! This is an educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL). For the unfamiliar:reinforcement learning(RL) is a machine learning approach for teaching agents how to solve tasks by trial and error. Deep RL refers to the combination of RL withdeep ...
How to parse PDF docs for RAG | OpenAI Cookbook
This notebook shows how to leverage GPT-4o to turn rich PDF documents such as slide decks or exports from web pages into usable content for your RAG application. This technique can be used if you have a lot of unstructured data containing valuable information that you want to be able to retrieve as part of your RAG pipeline.
We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude.
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