GPT-3: Language Models are Few-Shot Learners - GitHub 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
AntonOsika 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