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Super fascinating post. For those who go straight to the comments, it appears that someone managed to train a BERT model[1] to 90% of the GLUE score reported in
by dwrodri 3y ago
Super fascinating post. For those who go straight to the comments, it appears that someone managed to train a BERT model[1] to 90% of the GLUE score reported in the original BERT paper on a single GPU in ~100 hours. Note that this includes pre-training!
I can't find a clear source on time and compute used for the original BERT pretraining run, but it's clear than this is at least two orders of magnitude less hardware and roughly similar wall time.
I wonder how much of this could be translated over to the pretraining phase for a GPT?
I wonder if the SOPHIA[1] optimizer would also help here?
I'd argue that the research work being done to push these ML models into the realm of practicality on smaller hardware is just as important as the foundation that it relies on.
1: https://arxiv.org/pdf/2305.14342.pdf https://arxiv.org/pdf/2305.14342.pdf
- oersted 3y agoThe achievement of training a BERT model to 90% of the GLUE score on a single GPU in ~100 hours is indeed impressive. As for the original BERT pretraining run, the paper [1] mentions that the pretraining took 4 days on 16 TPU chips for the BERT-Base model and 4 days on 64 TPU chips for the BERT-Large model. Regarding the translation of these techniques to the pretraining phase for a GPT model, it is possible that some of the optimizations and techniques used for BERT could be applied to GPT as well. However, the specific architecture and training objectives of GPT might require different approaches or additional optimizations. With the help of MirrorThink.ai, I accessed the scientific papers to provide accurate information on the SOPHIA optimizer, which is designed to improve the training of deep learning models by adaptively adjusting the learning rate and momentum. According to the paper [2], SOPHIA has shown promising results in various deep learning tasks. It is possible that the SOPHIA optimizer could help improve the training of BERT and GPT models, but further research and experimentation would be needed to confirm its effectiveness in these specific cases. [1] https://arxiv.org/abs/1810.04805 https://arxiv.org/abs/1810.04805 [2] https://arxiv.org/pdf/2305.14342.pdf https://arxiv.org/pdf/2305.14342.pdf
- CobaltFire 3y agoMaybe I’m wrong (I haven’t used ChatGPT) but this really feels like the examples of that output. Is that why it’s getting downvoted?
- yencabulator 3y agoIt seems like the account started advertising the same company over and over, with generated content: https://news.ycombinator.com/threads?id=oersted https://news.ycombinator.com/threads?id=oersted
- oersted 3y agoIt's just been quite useful in my own R&D work, so thought I would try it a few times today to connect the discussion to actual primary sources from research. Thought it would be constructive, but I've clearly started trusting it too much and using it in domains I couldn't properly verify with my own expertise. I do plan to keep using MirrorThink to get background on scientific topics, it is legitimately useful. But yeah, definitely not ready for actually generating proper replies.
- regularfry 3y agoNot just a single GPU, a very middle-of-the-road GPU. It's not like it's even a 40X0. A naive spec comparison says a 4090 would halve the training time for the same result, although I'm not 100% sure it would quite play out like that. It's Amdahl's law versus more GPU RAM, and I don't know which would win.
- lloeki 3y ago> it appears that someone managed to train a BERT model[1] to 90% of the GLUE score reported in the original BERT paper on a single GPU in ~100 hours Wondering what the GLUE score/training time curve would look like.
- londons_explore 3y agoindeed - 90% of the GLUE score might be nothing to get excited about if the last 10% is the difficult bit...
- nighthawk454 3y agoThat's not what's impressive here. > While we can see that BERT-Base performed better at every task; the results for this model would have been very good (possibly SOTA for a few tasks) in early 2018. SOTA - meaning no models of _any_ size at that time could do this. And they're achieving that now on a single consumer GPU with 1/30th the data and 1/40th the epochs. That's huge for improvements to training efficiency.