this post was submitted on 07 May 2024
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Training your own will be very difficult. You will need to gather so much data to get a model that has basic language understanding.
What I would do (and am doing) is just taking something like llama3 or mistral and adding your own content using RAG techniques.
But fair play if you do manage to train a real model!
OLlama is so fucking slow. Even with a 16-core overclocked Intel on 64Gb RAM with an Nvidia 3080 10Gb VRAM, using a 22B parameter model, the token generation for a simple haiku takes 20 minutes.
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?
I think I fucked up my docker setup and will wipe and start over.
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. :-)
My setup is Win 11 Pro ➡️ WSL2 / Debian ➡️ Docker Desktop (for windows)
Should I install the nvidia drivers within Debian even though the host OS already has drivers?
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.
I did indeed follow that guide already, thank you for the respect; I am an idiot and installed both the nvidia WSL driver on top of the host OS driver _as well as the Cuda driver. So I'll try again with only that guide and see what breaks.
We all mess up! I hope that helps - let me know if you see improvements!
Hmmm weird. I have a 4090 / Ryzen 5800X3D and 64GB and it runs really well. Admittedly it's the 8B model because the intermediate sizes aren't out yet and 70B simply won't fly on a single GPU.
But it really screams. Much faster than I can read. PS: Ollama is just llama.cpp under the hood.
Edit: Ah, wait, I know what's going wrong here. The 22B parameter model is probably too big for your VRAM. Then it gets extremely slow yes.
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?
What is the appropriate size for 10Gb VRAM?
It depends on your prompt/context size too. The more you have the more memory you need. Try to check the memory usage of your GPU with GPU-Z with different models and scenarios.
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.
I agree. My 3070 runs the 8B Llama3 model in about 250ms, especially for short responses.