[2024 Best AI Paper] LLM Pruning and Distillation in Practice: The Minitron Approach

By Paper With Video

Community Score: 50% | 304 views | 1y

0 community ratings: null thumbs up, null thumbs down

This video was created using https://paperspeech.com. If you’d like to create explainer videos for your own papers, please visit the website! Title: LLM Pruning and Distillation in Practice: The Minitron Approach Authors: Sharath Turuvekere Sreenivas, Saurav Muralidharan, Raviraj Joshi, Marcin Chochowski, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro, Jan Kautz, Pavlo Molchanov Abstract: We present a comprehensive report on compressing the Llama 3.1 8B and Mistral NeMo 12B models to 4B and 8B parameters, respectively, using pruning and distillation. We explore two distinct pruning strategies: (1) depth pruning and (2) joint hidden/attention/MLP (width) pruning, and evaluate the results on common benchmarks from the LM Evaluation Harness. The models are then aligned with NeMo Aligner and tested in instruct-tuned versions. This approach produces a compelling 4B model from Llama 3.1 8B and a state-of-the-art Mistral-NeMo-Minitron-8B (MN-Minitron-8B for brevity) model from Mistral