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GPT-2: GPT-1, but with modified normalization 1.5 billion WebText: 40 GB of text, 8 million documents, from 45 million webpages upvoted on Reddit. February 14, 2019 (initial/limited version) and November 5, 2019 (full version) [40] "tens of petaflop/s-day", [41] or 1.5e21 FLOPS. [42] GPT-3: GPT-2, but with modification to allow larger scaling ...
The GPT-1 architecture was a twelve-layer decoder-only transformer, using twelve masked self-attention heads, with 64-dimensional states each (for a total of 768). Rather than simple stochastic gradient descent , the Adam optimization algorithm was used; the learning rate was increased linearly from zero over the first 2,000 updates to a ...
For example, training of the GPT-2 (i.e. a 1.5-billion-parameters model) in 2019 cost $50,000, while training of the PaLM (i.e. a 540-billion-parameters model) in 2022 cost $8 million, and Megatron-Turing NLG 530B (in 2021) cost around $11 million. [56] For Transformer-based LLM, training cost is much higher than inference cost.
Generative AI systems trained on words or word tokens include GPT-3, GPT-4, GPT-4o, LaMDA, LLaMA, BLOOM, Gemini and others (see List of large language models). They are capable of natural language processing, machine translation, and natural language generation and can be used as foundation models for other tasks. [62]
For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...
AutoGPT is an open-source "AI agent" that, given a goal in natural language, will attempt to achieve it by breaking it into sub-tasks and using the Internet and other tools in an automatic loop. [1] It uses OpenAI's GPT-4 or GPT-3.5 APIs, [2] and is among the first examples of an application using GPT-4 to perform autonomous tasks. [3]
PyTorch, an open-source Tensor and Dynamic neural network in Python. [84] TensorFlow, an open-source software library for machine learning. [85] Theano, a Python library and optimizing compiler for manipulating and evaluating mathematical expressions, especially matrix-valued ones. [86]
For AI alignment, reinforcement learning with human feedback (RLHF) was used with a combination of 1,418,091 Meta examples and seven smaller datasets. The average dialog depth was 3.9 in the Meta examples, 3.0 for Anthropic Helpful and Anthropic Harmless sets, and 1.0 for five other sets, including OpenAI Summarize, StackExchange, etc.