Code generation

Stefano Ermon discusses adapting diffusion models to language and code generation. Topics include discrete token spaces, parallel token generation, Mercury 2, and trade-offs with autoregressive models. The conversation examines training, serving and post-training challenges, controllable generation and possible directions for multimodal diffusion systems.

…need for high-performance simulation codes and a good user experience in terms of easy-to-use modeling of neurons and synapses, NESTML [2] was created as a domain-