tx · 3nnFoQA5qYppA148Btq3jpL8GfVfUUReSXWFqounNYnk
3N9ttyLcRwDo7L4EmJkbS3ZFuQJygivupsL: -0.00500000 Waves
2023.07.04 17:25 [2651274] invoke 3N9ttyLcRwDo7L4EmJkbS3ZFuQJygivupsL > 3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV commitChatGPTTask()
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: GgcMe7S8m1rHaAKLvpXGuuNT8w3T4QBfTQJfeJJmbh6z_92ovWCy1Zf8CSsTLLLssC74m8yn5yPMqVp9fmVacou97_commit_timestamp: 1688480759257
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: GgcMe7S8m1rHaAKLvpXGuuNT8w3T4QBfTQJfeJJmbh6z_92ovWCy1Zf8CSsTLLLssC74m8yn5yPMqVp9fmVacou97_commit_height: 2651274
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: GgcMe7S8m1rHaAKLvpXGuuNT8w3T4QBfTQJfeJJmbh6z_92ovWCy1Zf8CSsTLLLssC74m8yn5yPMqVp9fmVacou97_result: "The basic technology behind current Large Language Models, such as OpenAI's GPT-3, is deep learning. These models are typically built using a type of deep learning architecture called a transformer.
Transformers are neural networks specifically designed for dealing with sequential data, such as text. They use attention mechanisms to capture relationships between different words or tokens in a sentence. This attention mechanism allows the model to consider the context of each word within the entire input sequence.
Large Language Models are trained in a two-step process: pre-training and fine-tuning. During pre-training, the model is exposed to a large corpus of text from the internet and learns to predict the next word in a sentence. This process helps the model in capturing the statistical patterns and linguistic structures of the language.
After pre-training, the model is fine-tuned on specific tasks or domains. For example, it can be fine-tuned on tasks like language translation, question-answering, or generating text in a particular style. Fine-tuning allows the model to adapt its pre-trained knowledge to the specific requirements of the target task.
The large-scale compute infrastructure, consisting of powerful GPUs or TPUs, is crucial in training and running these massive models efficiently. These models have millions or even billions of parameters, which enable them to capture complex language patterns and generate coherent and contextually relevant responses."
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: GgcMe7S8m1rHaAKLvpXGuuNT8w3T4QBfTQJfeJJmbh6z_92ovWCy1Zf8CSsTLLLssC74m8yn5yPMqVp9fmVacou97_status: "checked_out" -> "done"
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