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So whatamp;#39;s the problem with training large language models and fine tuning them, the key thing here is that we end up with really big weights. this raises a whole bunch of problems here. These problems are two main things. One, you need a lot more compute to train. For this And as the models are getting larger and larger, you are finding that you need much bigger GPUs multiple GPUs just to be able to fine tune some of these models. The second problem is that in addition to basically needing the compute. The file sizes become huge. So, The T five. X xl. check point is around about 40 gigabytes in size. Not to mention, the sort of 20 billion parameter models that weamp;#39;ve got coming out now. Are getting bigger and bigger all the time. So, this is where this idea of parameter efficient, fine tuning comes in. So Iamp;#39;m just gonna talk about this as PeFT going forward. So PeFT uses a variety of different techniques. The one weamp;#39;re gonna be lo
