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Approach where, starting from a single pretrained model, multiple progressively smaller models can be obtained. For example, a 15B model can be pruned and distilled to obtain an 8B model, which in turn serves as a starting point for pruning and distilling a 4B model, and so on. The combination of model pruning followed by light retraining through distillation is an effective and cost-efficient approach to train a family of models. For each additional model, just 100-400B tokens are used for retraining—a greater than 40x reduction compared to training from scratch. As such, the compute cost savings to train a family of models (12B, 8B, and 4B) is up to 1.95x compared to training all models from scratch. The learning from extensive ablation studies has been summarized into 10 best practices for structured weight pruning combined with knowledge distillation. We found that width pruning consistently outperforms depth pruning and, most importantly, pruned and distilled models outperform models trained from scratch in quality. Mistral-NeMo-Minitron 8BFollowing our best practices, we width-pruned the Mistral NeMo 12B model to obtain an 8B target model. This section details the steps and parameters used to obtain the Mistral-NeMo-Minitron 8B base model, as well as its performance.Teacher fine-tuningTo correct for the distribution shift across the original dataset the model was trained on, we first fine-tuned the unpruned Mistral NeMo 12B model on our dataset using 127B tokens. Experiments showed that, without correcting for the distribution shift, the teacher provides suboptimal guidance on the dataset when being distilled.Width-only pruningGiven our goal of obtaining the strongest 8B model possible, we proceeded with width-only pruning. We pruned both the embedding (hidden) and MLP intermediate dimensions along the width axis to compress Mistral NeMo 12B. Specifically, we computed importance scores for each attention head, embedding channel, and MLP hidden dimension using the activation-based strategy. Following importance estimation, we:Pruned the MLP intermediate dimension from 14336 to 11520Pruned the hidden size from 5120 to 4096Retained the attention headcount and number of layersDistillation parametersWe distilled the model with peak learning rate=1e-4, minimum learning rate=4.5e-7, linear warm up of 60 steps, cosine decay schedule, and
