со скольки можно отдать ребенка в кадетское училище
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со скольки можно отдать ребенка в кадетское училище, GPT-3: Language Models are Few-Shot Learners - GitHub, o1、GPT4、GPT4o 这三个有什么区别？ - 知乎, gpt-engineer - GitHub, 最新ChatGPT Plus合租攻略：国内最靠谱的ChatGPT Plus拼车平台推荐 (每月仅27元)！可使用GPT-4o生图功能和GPT , 国内如何使用 ChatGPT？最容易懂的 ChatGPT 介绍与教学指南, GitHub - openai/gpt-2: Code for the paper "Language Models are , RVC-Boss/GPT-SoVITS - GitHub.
Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions – something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general., GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic., GPT-4o 有强大的多模态处理能力，能同时处理文本、图像和音频输入；响应速度提升，支持更自然的实时对话和多种媒体的结合；整合了 Canvas，支持可视化的工作环境，可实时修改文本或代码。 P.S.：GPT-4o 体现了 OPEN AI 期望将全模态统一在 Transformer 框架下。 GPT-4o .
gpt-engineer installs the binary 'bench', which gives you a simple interface for benchmarking your own agent implementations against popular public datasets. The easiest way to get started with benchmarking is by checking out the template repo, which contains detailed instructions and an agent template., 最新ChatGPT Plus合租攻略：国内最靠谱的ChatGPT Plus拼车平台推荐(每月仅27元)！可使用GPT-4o生图功能和GPT-4.1系列模型，还支持满血版DeepSeek-R1、马斯克的Grok-3和谷歌Gemini-2.5 Pro！如果你无法解决科学上网的问题，或觉得每月20美元的会员费用过高，可以考虑ChatGPT Plus共享合租帐号。这种方式不仅能够降低 , 这是一份全方位的指南，帮助您轻松使用 ChatGPT 中文版，无需科学上网即可体验 GPT-4 的全部功能！ 在本文中，您将了解如何通过推荐的镜像网站访问 ChatGPT 中文版、注册使用的具体步骤，以及常见问题的解答。, gpt-2 Code and models from the paper "Language Models are Unsupervised Multitask Learners". You can read about GPT-2 and its staged release in our original blog post, 6 month follow-up post, and final post. We have also released a dataset for researchers to study their behaviors., 1 min voice data can also be used to train a good TTS model! (few shot voice cloning) - RVC-Boss/GPT-SoVITS, 更新日期：2025/06/20 国内能用的 ChatGPT 中文版 、 gtp， 无需翻墙， 支持 GPT-4、4o、o1 及其他中文功能。 本指南提供了详尽的 ChatGPT 中文版使用建议，涵盖国内可用的 ChatGPT 镜像网站 推荐和详细使用教程，帮助您快速掌握 ChatGPT 的使用。.