Stanford CS25: V4 I Demystifying Mixtral of Experts скачать в хорошем качестве

Stanford CS25: V4 I Demystifying Mixtral of Experts

April 25, 2024 Speaker: Albert Jiang, Mistral AI / University of Cambridge Demystifying Mixtral of Experts In this talk I will introduce Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model. Mixtral has the same architecture as Mistral 7B, with the difference that each layer is composed of 8 feedforward blocks (i.e. experts). For every token, at each layer, a router network selects two experts to process the current state and combines their outputs. Even though each token only sees two experts, the selected experts can be different at each timestep. As a result, each token has access to 47B parameters, but only uses 13B active parameters during inference. I will go into the architectural details and analyse the expert routing decisions made by the model. About the speaker: Albert Jiang is an AI scientist at Mistral AI, and a final-year PhD student at the computer science department of Cambridge University. He works on language model pretraining and reasoning at Mistral AI, and language models for mathematics at Cambridge. More about the course can be found here: https://web.stanford.edu/class/cs25/ View the entire CS25 Transformers United playlist:    • Stanford CS25 - Transformers United