coral-pi-rest-server VS rwkv.cpp

That said, you can always look at something like https://coral.ai/products/accelerator/ to help with the performance you need.

Should only be ~$60 https://coral.ai/products/accelerator/

The Coral USB accelerator might be of interest if you want to do some light ML with a low power budget.

However, a pi doesn't have the strength to run something like Llama.cpp, of course, so I've been considering using something like the Coral USB Accelerator (https://coral.ai/products/accelerator). As I've been learning more about it, it seems to be very geared towards TensorFlow Lite models. But whisper.cpp and Llama.cpp use ggml models.

Recently found this thing, though I haven't found a use case for me.

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)

Q8_0 models: only for https://github.com/saharNooby/rwkv.cpp (fast CPU).

There's https://github.com/saharNooby/rwkv.cpp which seems to work, and might be compatible with text-generation-webui.

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.

I'm most interested in that last one. I think I heard the RWKV models are very fast, don't need much Ram, and can have huge context tokens, so maybe their 14b can work for me. I wasn't sure how ready for use they were though, but looking more into it, stuff like rwkv.cpp and ChatRWKV and a whole lot of other community projects are mentioned on their github.