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A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
Other than scaling up training compute, one can also scale up inference compute. As an example, the Elo rating of AlphaGo improves steadily as it is allowed to spend more time on its Monte Carlo Tree Search per play. [24]: Fig 4 For AlphaGo Zero, increasing Elo by 120 requires either 2x model size and training, or 2x test-time search. [25]
Trained for 3 months on over 2000 A100 GPUs on the NVIDIA Selene Supercomputer, for over 3 million GPU-hours. [30] Ernie 3.0 Titan: December 2021: Baidu: 260 [31] 4 Tb Proprietary Chinese-language LLM. Ernie Bot is based on this model. Claude [32] December 2021: Anthropic: 52 [33] 400 billion tokens [33] beta Fine-tuned for desirable behavior ...
The number of neurons in the middle layer is called intermediate size (GPT), [55] filter size (BERT), [35] or feedforward size (BERT). [35] It is typically larger than the embedding size. For example, in both GPT-2 series and BERT series, the intermediate size of a model is 4 times its embedding size: =.
It is named "chinchilla" because it is a further development over a previous model family named Gopher. Both model families were trained in order to investigate the scaling laws of large language models. [2] It claimed to outperform GPT-3. It considerably simplifies downstream utilization because it requires much less computer power for ...
[49] [50] [51] The model files were officially removed on March 21, 2023, over hosting costs and safety concerns, though the code and paper remain online for reference. [ 52 ] [ 53 ] [ 54 ] Meditron is a family of Llama-based finetuned on a corpus of clinical guidelines, PubMed papers, and articles.
the feed-forward size and filter size are synonymous. Both of them denote the number of dimensions in the middle layer of the feed-forward network. the hidden size and embedding size are synonymous. Both of them denote the number of real numbers used to represent a token. The notation for encoder stack is written as L/H.
At the time of the MMLU's release, most existing language models performed around the level of random chance (25%), with the best performing GPT-3 model achieving 43.9% accuracy. [3] The developers of the MMLU estimate that human domain-experts achieve around 89.8% accuracy. [ 3 ]