Unraveling the Mystery: Non-determinism in GPT-4 Explained by Sparse MoE скачать в хорошем качестве

Unraveling the Mystery: Non-determinism in GPT-4 Explained by Sparse MoE

The author of the post discusses the non-deterministic behavior of GPT-4 and GPT-3.5-turbo models and proposes that the sparse Mixture of Experts (MoE) architecture used in these models is the cause. They conduct experiments using the OpenAI API to test their hypothesis and find that GPT-4 produces a significantly higher number of unique completions compared to other models. The comments discuss the determinism of GPUs and the role of software in introducing non-determinism. Some argue that GPUs are deterministic but certain primitives used in AI/ML code can introduce non-determinism, while others argue that GPUs are inherently non-deterministic due to their design. There is also discussion about the trade-off between determinism and performance in AI models. 🔗 https://152334H.github.io/blog/non-de... #AI #GPT