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It has a context length of 128k tokens [15] with an output token limit capped to 4,096, [16] and after a later update (gpt-4o-2024-08-06) to 16,384. [ 17 ] As of May 2024, it is the leading model in the LMSYS Elo Arena Benchmarks by the University of California, Berkeley .
Generative Pre-trained Transformer 4 (GPT-4) is a multimodal large language model trained and 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 Microsoft Copilot. [2]
A fine-tuned variant of GPT-3, termed GPT-3.5, was made available to the public through a web interface called ChatGPT in 2022. [22] GPT-Neo: March 2021: EleutherAI: 2.7 [23] 825 GiB [24] MIT [25] The first of a series of free GPT-3 alternatives released by EleutherAI. GPT-Neo outperformed an equivalent-size GPT-3 model on some benchmarks, but ...
Wordkraft is powered by OpenAI’s GPT-3, GPT-3.5, and GPT-4 algorithms. In this case, they have been fine-tuned to produce more accurate and relevant content for Wordkraft users.
The second generation of Gemini ("Gemini 1.5") has two models. Gemini 1.5 Pro is a multimodal sparse mixture-of-experts, with a context length in the millions, while Gemini 1.5 Flash is distilled from Gemini 1.5 Pro, with a context length above 2 million. [46] Gemma 2 27B is trained on web documents, code, science articles.
Other models with large context windows includes Anthropic's Claude 2.1, with a context window of up to 200k tokens. [46] Note that this maximum refers to the number of input tokens and that the maximum number of output tokens differs from the input and is often smaller. For example, the GPT-4 Turbo model has a maximum output of 4096 tokens. [47]
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] 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.
The series includes 4 models, 2 base models (DeepSeek-V2, DeepSeek-V2-Lite) and 2 chatbots (DeepSeek-V2-Chat, DeepSeek-V2-Lite-Chat). The two larger (not Lite) models were trained as follows: [51] Pretrain on a dataset of 8.1T tokens, using 12% more Chinese tokens than English ones. Extend context length from 4K to 128K using YaRN. [52]