4 ms·

Stats from TFA say 3 hours to fine tune on an A100 processor.

• all2 4y ago
  I wonder if one could "fine tune" specific layer values... That might be faster than updating every weight in every layer.
  • nico 4y ago
    Or “multiplex” fine-tuning with inference, ie. do fine-tuning for 100ms, inference for 100ms, then tuning again… etc Btw, is there a way to combine two or more models? So for example, if I create 5 copies of a model, then fine-tune each copy with a different dataset -> can the 5 datasets be merged together somehow to create a model that has the learning of the 5?
  • nl 4y ago
    LoRA is an alternative to traditional fine tuning (which is usually done on specific layers as you mentioned). To quote the LoRA paper[1]: > We hypothesize that the change in weights during model adaptation also has a low “intrinsic rank”, leading to our proposed Low-Rank Adaptation (LoRA) approach. LoRA allows us to train some dense layers in a neural network indirectly by optimizing rank decomposition matrices of the dense layers’ change during adaptation instead, while keeping the pre-trained weights frozen It's truly revolutionary: It basically lets you create a very small "diff" which you apply yo an existing model and it is suddenly fine tuned. These diff models are very small (5M for example). [1] https://arxiv.org/abs/2106.09685 https://arxiv.org/abs/2106.09685