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.

[0] https://github.com/vgel/repeng/issues

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

Has there been progress towards making RWKV multimodal? Can be use projector layers to send images to RWKV?

There is work done for Vision RWKV, and audio RWKV, an example paper is here: https://arxiv.org/abs/2403.02308

Its the same principle as open transformer models where an adapter is used to generate the embedding

However currently the core team focus is in scaling the core text model, as this would be the key performance driver, before adapting multi-modal.

The tech is there, the base model needs to be better

Thanks! The 0.1B version looks perfect for embedded system. What is the key benefit of attention-free architecture?

lower compute cost especially over longer sequence length. Depending on context length, its 10x, 100x, or even 1000x+ cheaper. (quadratic vs linear cost difference)

Do you have an in depth comparison between RWKV and models like mamba or s4?

Not sure how indepth you want it to be. But we did do a co-presentation with one of the coauthors of mamba at latent space : https://www.youtube.com/watch?v=LPe6iC73lrc

congrats and great work on RWKV and Recursal.ai

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?

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.

What would be the most performant way to run a inference using RWKV? Do you have and speed comparison to a similar sized transformer?

I have a task(OCR cleaning) that I´m evaluating faster options and look like RWKV would be a nice alternative.

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.)

Example use of Gutenberg:

https://www.tensorflow.org/datasets/catalog/pg19

> 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.