xcjs

Getting Keycloak and Headscale working together.

But I did it after three weeks.

I captured my efforts in a set of interdependent Ansible roles so I never have to do it again.

It would be extremely barebones, but you can do something like this with Pandoc.

That I agree with. Microsoft drafted the recommendation to use it for local networks, and Apple ignored it or co-opted it for mDNS.

Macs aren’t the only thing that use mDNS, either. I have a host monitoring solution that I wrote that uses it.

Yeah, that’s why I started using .lan.

I was using .local, but it ran into too many conflicts with an mDNS service I host and vice versa. I switched to .lan, but I’m certainly not going to switch to .internal unless another conflict surfaces.

I’ve also developed a host-monitoring solution that uses mDNS, so I’m not about to break my own software. 😅

Coincidentally, I just found this other thread that mentions EasyEffects: https://programming.dev/post/17612973

You might be able to use a virtual device to get it working for your use case.

I just wanted to update this to mention that there are a lot of custom low level performance improvements for CPU based inferencing in Llamafile: https://justine.lol/matmul/

It’s just a different use case to create a single-file large language model engine that automatically chooses the “best” parameters to run under. It uses llama.cpp under the hood.

The intent is to make it as easy as double clicking a binary to get up and running.

It depends on the model you run. Mistral, Gemma, or Phi are great for a majority of devices, even with CPU or integrated graphics inference.

I’m also going to push forward Tilda, which has been my preferred one for a while due to how minimal the UI is.

We all mess up! I hope that helps - let me know if you see improvements!

I think there was a special process to get Nvidia working in WSL. Let me check… (I’m running natively on Linux, so my experience doing it with WSL is limited.)

https://docs.nvidia.com/cuda/wsl-user-guide/index.html - I’m sure you’ve followed this already, but according to this, it looks like you don’t want to install the Nvidia drivers, and only want to install the cuda-toolkit metapackage. I’d follow the instructions from that link closely.

You may also run into performance issues within WSL due to the virtual machine overhead.

Good luck! I’m definitely willing to spend a few minutes offering advice/double checking some configuration settings if things go awry again. Let me know how things go. :-)

It should be split between VRAM and regular RAM, at least if it’s a GGUF model. Maybe it’s not, and that’s what’s wrong?

Ok, so using my “older” 2070 Super, I was able to get a response from a 70B parameter model in 9-12 minutes. (Llama 3 in this case.)

I’m fairly certain that you’re using your CPU or having another issue. Would you like to try and debug your configuration together?

Unfortunately, I don’t expect it to remain free forever.

No offense intended, but are you sure it’s using your GPU? Twenty minutes is about how long my CPU-locked instance takes to run some 70B parameter models.

On my RTX 3060, I generally get responses in seconds.

My go-to solution for this is the Android FolderSync app with an SFTP connection.

I mean, sysvinit was just a bunch of root-executed bash scripts. I’m not sure if systemd is really much worse.