- Awesome GPT - GitHub
Awesome GPT A curated list of awesome projects and resources related to GPT, ChatGPT, OpenAI, LLM, and more
- GitHub - openai gpt-oss: gpt-oss-120b and gpt-oss-20b are two open . . .
Try gpt-oss · Guides · Model card · OpenAI blog Download gpt-oss-120b and gpt-oss-20b on Hugging Face Welcome to the gpt-oss series, OpenAI's open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases We're releasing two flavors of these open models: gpt-oss-120b — for production, general purpose, high reasoning use cases that fit into a single
- GPT-3: Language Models are Few-Shot Learners - GitHub
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
- 可以详细说下从GPT-1到GPT-4，有哪些变化，是如何发展的？ - 知乎
GPT-3不仅能生成连贯的段落，而且能生成整篇与上下文相关、风格一致的文章，这些文章通常与人类编写的内容无法区分。 GPT-3具有零样本学习的能力，即使在没有经过特定训练的情况下，也能执行特定任务，它的出现使得AI语言模型的应用得到了广泛的推广。
- GitHub - openai gpt-2: Code for the paper Language Models are . . .
The dataset our GPT-2 models were trained on contains many texts with biases and factual inaccuracies, and thus GPT-2 models are likely to be biased and inaccurate as well To avoid having samples mistaken as human-written, we recommend clearly labeling samples as synthetic before wide dissemination
- GitHub - binary-husky gpt_academic: 为GPT GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读 . . .
GPT 学术优化 (GPT Academic) 如果喜欢这个项目，请给它一个Star；如果您发明了好用的快捷键或插件，欢迎发pull requests！ If you like this project, please give it a Star Read this in English | 日本語 | 한국어 | Русский | Français All translations have been provided by the project itself
- OpenAI 发布三款 GPT-4. 1 系列模型，性能有哪些提升？对行业来说，其最大吸引力是什么？ - 知乎
在 Aider 的多语言 diff 基准测试中，GPT‑4 1 的得分是 GPT‑4o 的两倍以上，甚至比 GPT‑4 5 也高出 8 个百分点。 这项评估不仅衡量模型在多种编程语言下的编码能力，也衡量其生成完整代码或 diff 格式变更的能力。
- gpt-engineer - GitHub
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
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