Last month, we announced Llama 3.1, which includes our largest model yet, the 405B, as well as two smaller models with 70 billion and 8 billion parameters, respectively. Smaller models from a larger relative are typically cheaper to deploy to the masses and perform well across many language tasks. In a new research paper, our partners at NVIDIA explore how various large models can be made smaller using structured weight pruning and knowledge distillation—without having to train a new model from scratch. Working with Llama 3.1 8B, the team shares how it created Llama-Minitron 3.1 4B, its first work within the Llama 3.1 open source family.