Selfhosted

40132 readers

544 users here now

A place to share alternatives to popular online services that can be self-hosted without giving up privacy or locking you into a service you don't control.

Rules:

Be civil: we're here to support and learn from one another. Insults won't be tolerated. Flame wars are frowned upon.
No spam posting.
Posts have to be centered around self-hosting. There are other communities for discussing hardware or home computing. If it's not obvious why your post topic revolves around selfhosting, please include details to make it clear.
Don't duplicate the full text of your blog or github here. Just post the link for folks to click.
Submission headline should match the article title (don’t cherry-pick information from the title to fit your agenda).
No trolling.

Resources:

selfh.st Newsletter and index of selfhosted software and apps
awesome-selfhosted software
awesome-sysadmin resources
Self-Hosted Podcast from Jupiter Broadcasting

Any issues on the community? Report it using the report flag.

Questions? DM the mods!

founded 1 year ago

MODERATORS

[email protected]

Self hosted LLM (sh.itjust.works)

submitted 8 months ago by [email protected] to c/[email protected]

18 comments fedilink hide all child comments

Hello internet users. I have tried gpt4all and like it, but it is very slow on my laptop. I was wondering if anyone here knows of any solutions I could run on my server (debian 12, amd cpu, intel a380 gpu) through a web interface. Has anyone found any good way to do this?

you are viewing a single comment's thread
view the rest of the comments

[–] [email protected] 4 points 8 months ago (1 children)

Have you been able to use it with your AMD GPU? I have a 6800 and would like to test something

[–] [email protected] 3 points 8 months ago* (last edited 8 months ago)

Yes, since we have similar gpus you could try the following to run it in a docker container on linux, taken from here and slightly modified:

#!/bin/bash

model=microsoft/phi-2
# share a volume with the Docker container to avoid downloading weights every run
volume=<path-to-your-data-directory>/data

docker run -e HSA_OVERRIDE_GFX_VERSION=10.3.0 -e PYTORCH_ROCM_ARCH="gfx1031" --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.4-rocm --model-id $model

Note how the rocm version has a different tag and that you need to mount your gpu device into the container. The two environment variables are specific to my (any maybe yours also) gpu architecture. It will need a while to download though.