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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
GitHub - formulahendry awesome-gpt: A curated list of awesome projects . . . This repository is a collection of awesome projects and resources related to GPT, ChatGPT, OpenAI, LLM, and other related technologies Whether you're just getting started with GPT or you're a seasoned expert, this list has something for everyone
Supported AI models in GitHub Copilot For all of the default AI models, input prompts and output completions run through GitHub Copilot's content filters for harmful, offensive, or off-topic content, and for public code matching when enabled Supported AI models in Copilot This table lists the AI models available in Copilot, along with their release status and availability in different modes
GitHub - binary-husky gpt_academic: 为GPT GLM等LLM大 . . . 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
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
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