rwkv.cpp

I didn’t know what RWKV.cpp was, so hopefully this helps others: https://github.com/RWKV/rwkv.cpp > INT4/INT5/INT8 and FP16 inference on CPU for RWKV language model

>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 .

There's https://github.com/saharNooby/rwkv.cpp, which related-ish[0] to ggml/llama.cpp [0]: https://github.com/ggerganov/llama.cpp/issues/846

Quantisation thankfully is applicable to RWKV as much as transformers. Most notably in our RWKV.cpp community project: https://github.com/saharNooby/rwkv.cpp Tooling/Ecosystem is something that I am actively working on as there is still a gap to transformers level of tooling. But i'm glad that there is a noticeable difference! And yes! experiments are important, to ensure improvements in the architecture. Even if "Linear Transformers" replaces "Transformers". Alternatives should always be explored, to learn from such trade-offs to the benefit of the ecosystem (This was lightly covered in the podcast, where I share IMO that we should have more research into text based diffusion networks)

Its greedy and random :) Instead of a paper, I would recommend the algorithms of most LMM implementations (rwkv.cpp has a relatively clean implementation in python https://github.com/saharNooby/rwkv.cpp/blob/master/rwkv/samp...)

Not actually, you might see here how bigger models have much worse perplexity with 4bit due to the weight outliers: https://github.com/saharNooby/rwkv.cpp/issues/12 For LLaMA models - yeah, different story.

Also worth checking out https://github.com/saharNooby/rwkv.cpp which is based on Georgi's library and offers support for the RWKV family of models which are Apache-2.0 licensed.