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Like RWKV and Mamba, this is mixing some RNN properties to avoid the issues transformers have. However I'm curious about their scaling claims. They have a plot
by VHRanger 3y ago
Like RWKV and Mamba, this is mixing some RNN properties to avoid the issues transformers have.
However I'm curious about their scaling claims. They have a plot that shows how the model scales in training with the FLOPs you throw at it.
But the issue we should rather be concerned with is the wall time of training for a set amount of hardware.
Back in 2018, we could train medium sized RNNs, the issue was with wall time of training and training stability.
- whimsicalism 3y agotransformers were also just better at the LM task than 2018 RNNs for equal amount of flop training
- VHRanger 3y agoYeah, that's just the training stability part to my knowledge
- whimsicalism 3y agothey're also just less capable models. like just adding attention on top of an RNN made them a lot better
- SpaceManNabs 3y agoCalculating self-attention is still quadratic though. So you get the negatives of transformers there too.
- foota 3y agoDo you know the downside with RWKV? Based on how they present it, it seems like the best thing since sliced bread, but I would have assumed that it would have been widely adopted if that were the case.
- VHRanger 3y agoIt seems only OK as a model? Looking at the LLM chat leaderboard it's 71st and the 14B version is worse than a lot of 7B models: https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboar... Also, llama.cpp makes inference accessible for a lot of people, and it's not available for RWKV. Not to knock on the model, I'm sure it's good. I also like that it's a succesful example of citizen science. It's just not popular enough to have the inference infrastructure transformers have, not established enough to attract enough money to get 60B+ models trained, and so on.
- whimsicalism 3y agoi believe it is undertrained, at minimum
- WanderPanda 3y agoThis leaderboard is not the best for comparing model architectures, the dataset and finetuning have too much influence. I think perplexity on a particular dataset would be a better way to compare
- logicchains 3y ago>Also, llama.cpp makes inference accessible for a lot of people, and it's not available for RWKV. It absolutely is: https://github.com/RWKV/rwkv.cpp https://github.com/RWKV/rwkv.cpp .
- jimmyl02 3y agoFrom what I know about RWKV, it's mostly a one man effort and doesn't have the same data pipeline / resources as most major labs. It's a bit unfortunate but I'm curious about the performance given the same training corpus as OpenAI's GPTs. Maybe some labs have tried internally but haven't released results? On the other hand it makes sense to invest more money into transformer training runs as they have been proven to work. They really burst onto the scene and brought back RNNs in the world of transformers. The claim that RWKV isn't paralleizable during training also seems to be refuted in their readme. I'd guess it's generalizable performance as there is a difference between doing well on benchmarks and being usable. Personally I've tried running the weights a long time ago when it was first released and the results weren't usable but I'm sure there has been considerable progress since then.
- GaggiX 3y agoThe paper shows that the speed is comparable to transformer models, faster with smaller with "long" sequence length like 8k.