This weekend I enjoyed the Fall vibes while reading the interesting paper by NVIDIA researchers "LLM Pruning and Distillation in Practice: The Minitron Approach". The paper presents an innovative approach to compressing large language models (LLMs) by combining pruning and distillation techniques. Key takeaways are: 1- Mistral-NeMo-Minitron-8B outperforms Llama 3.1 8B using 40x fewer training tokens! 2- Llama-3.1-Minitron 4Bn performs competitively with its teacher model Llama 3.1 8B. 3- Width pruning outperforms depth pruning when optimizing for parameter efficiency. This is exciting because compressing LLMs opens doors for more efficient deployment in resource-constrained environments, making AI more accessible! https://lnkd.in/e_s47Ph7
"Great insights! The combination of pruning and distillation is definitely a game-changer for optimizing LLMs, especially when the Minitron approach shows such significant gains in efficiency. The fact that Mistral-NeMo-Minitron-8B can outperform larger models with 40x fewer training tokens is impressive. It’s exciting to think how this could enable broader AI deployment in real-world applications where resources are limited. The potential impact on democratizing access to AI technology is huge!"
Jens Nestel
3mo
Impressive work simplifying complex models. Their insights raise intriguing possibilities. How might width pruning impact knowledge retention? Eager to discuss further.
Technical Fellow @ Walmart | AI & Relevance
3mo> 1- Mistral-NeMo-Minitron-8B outperforms Llama 3.1 8B using 40x fewer training tokens! We have to be careful here: I really don't like the way they phrase this - their 8B param model is of course based on first pruning down from a much bigger model which is not pretrained on 40x fewer tokens than Llama 3.1 8B. It's only the post-pruning continued pretraining that is so small. There's no free lunch: you still have to start with a heavily token-intensive pretraining model.