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ChatGPT is a chatbot and virtual assistant developed by OpenAI and launched on November 30, 2022. Based on large language models (LLMs), it enables users to refine and steer a conversation towards a desired length, format, style, level of detail, and language. Successive user prompts and replies are considered at each conversation stage as context.
Lambdalabs estimated a hypothetical cost of around $4.6 million US dollars and 355 years to train GPT-3 on a single GPU in 2020, [16] with lower actual training time by using more GPUs in parallel. Sixty percent of the weighted pre-training dataset for GPT-3 comes from a filtered version of Common Crawl consisting of 410 billion byte-pair ...
Website. openai .com /gpt-4. Generative Pre-trained Transformer 4 ( GPT-4) is a multimodal large language model created by OpenAI, and the fourth in its series of GPT foundation models. [ 1] It was launched on March 14, 2023, [ 1] and made publicly available via the paid chatbot product ChatGPT Plus, via OpenAI's API, and via the free chatbot ...
2. Define your target audience. The more context you can provide in your prompt, the better—and this includes who might read ChatGPT's responses. For example, if you use ChatGPT to outline a ...
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] [18] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
GPT-2 was pre-trained on a dataset of 8 million web pages. [2] It was partially released in February 2019, followed by full release of the 1.5-billion-parameter model on November 5, 2019. [3] [4] [5] GPT-2 was created as a "direct scale-up" of GPT-1 [6] with a ten-fold increase in both its parameter count and the size of its training dataset. [5]
Chinchilla contributes to developing an effective training paradigm for large autoregressive language models with limited compute resources. The Chinchilla team recommends that the number of training tokens is twice for every model size doubling, meaning that using larger, higher-quality training datasets can lead to better results on ...
The Pile (dataset) The Pile is an 886.03 GB diverse, open-source dataset of English text created as a training dataset for large language models (LLMs). It was constructed by EleutherAI in 2020 and publicly released on December 31 of that year. [ 1][ 2] It is composed of 22 smaller datasets, including 14 new ones. [ 1]