I view it more as a shortcut. We have trained 7B and 14B models from scratch, matching transformer performance with similar sized datasets.
This has been shown to even slightly outperform transformer scaling law, with the training we done from 1B to 14B. And we expect it to do so as we scale.
However as of this point, answering and settling that debate for good at 72B scale - is a $5 Million dollar bill. So for now, we use the short cuts, to just show that it actually works - and use that money to iterate and improve the architecture faster.
RWKV already solve the parallel compute problem for GPU, based on the changes it has done - so it is a recurrent model that can scale to thousands++ of GPU no issue.
Arguably with other recurrent architecture (State Space, etc) with very different design implementation. The issue of old recurrent design was just the way LSTM was designed. Not the recurrent nature.
This is a full drop in replacement for any transformer model use cases on model sizes 32B and under, as it has equal performance to existing open 32B models in most benchmarks
We are in works on a 70B, which will be a full drop in replacement for most text use cases
There is a current lack of "O1 style" reasoning dataset in open source space. QWQ did not release their dataset. So that would take some time for the community to prepare.
It's definitely something we are tracking to do as well =)
I noticed the lack of support from ollama and llama.cpp for RWKV. As those are (to my eyes) very strong drivers of experimentation (i.e. supporting them means vastly more outreach) I was considering whether you were considering taking this into your own hands by contributing code to them? Or rather is the fact that you are not (AFAIK) doing it because you lack the bandwidth in terms of man power or any other reason?
It’s really, interesting work. I’m glad you’ve kept at it. I’d like to ask you about two issues.
I keep seeing papers like “Repeat After Me” claiming serious weaknesses of state space vs transformer models. What are the current weaknesses of RWKV vs transformers? Have you mitigated them? If so, how?
The other issue is that file sharing being illegal, Wikipedia requiring derivatives to be copyleft, etc means I can’t train models with most data legally. Pre-1920’s works in Project Gutenberg are totally public domain. Both the model and the training data would be 100% legal for reproducible research. Would your team be willing to train a 3B-7B model on only Gutenberg and release it to the public domain?
(Note: The Stack without GitHub Issues can be used for permissive code. However, there could be contamination issues like incorrect licenses, PII, etc. So, maybe at least one, 100% legal model. Maybe a second with Gutenberg and The Stack for coding research.)
> The other issue is that file sharing being illegal, Wikipedia requiring derivatives to be copyleft, etc means I can’t train models with most data legally.
That really depends on whether LLM pretraining ends up held as an infringing use. (Of course, it’ll take a while for the cases to work through the courts and for a body of jurisprudence to be developed on this subject.)
There’s two legal issues: sharing copyrighted data; training on it. It’s the latter that’s ambiguous. My problem is the former.
Making copies of and sharing copyrighted works without the authors’ permission is already illegal as proven in countless, file-sharing cases. The AI trainers do this with data sets like Common Crawl, The Pile, and RefinedWeb. Just sharing them is illegal for most of the content in them.
I got ideas for how to deal with that in countries with TDM exceptions, like Singapore. For now, the only things we can share with others for model training are (a) public domain works and (b) content licensed for permissive use and sharing. Gutenberg entries before a certain year should be pretty risk-free.
I'm quite interested in repeng [0] (representztion engineering) for steerability of (so fzr transformer based) LLMs and was wondering if anyone had tried such methods on rwkv (or mamba for that matter). Maybe there are some low hanging fruits about it.
One of the interesting "new direction" for RWKV and Mamba (or any recurrent model), is the monitoring and manipulation of the state in between token. For steerability, alignment, etc =)
Not saying its a good or bad idea, but pointing out that having a fixed state in between has interesting applications in this space
lower compute cost especially over longer sequence length. Depending on context length, its 10x, 100x, or even 1000x+ cheaper. (quadratic vs linear cost difference)
Has there been any plans to build a “reasoning” llm using RWKV? With the increase in inference token count caused by such methods, the muhc lower footprint of recurrent architecture could really make a difference for such a use-case.
This is actually the hypothesis for cartesia (state space team), and hence their deep focus on voice model specifically. Taking full advantage of recurrent models constant time compute, for low latencies.
RWKV team's focus is still however is first in the multi-lingual text space, then multi-modal space in the future.
Tons of VC money burned in pursuit of low-probability success. It’s no wonder that some people find it easier to scam VCs than it is to build a real business.
I view it more as a shortcut. We have trained 7B and 14B models from scratch, matching transformer performance with similar sized datasets.
This has been shown to even slightly outperform transformer scaling law, with the training we done from 1B to 14B. And we expect it to do so as we scale.
However as of this point, answering and settling that debate for good at 72B scale - is a $5 Million dollar bill. So for now, we use the short cuts, to just show that it actually works - and use that money to iterate and improve the architecture faster.