First, here is my SimpleTuner LoRA config, so you can get started with it. I used an 80GB A100 (thanks @bghira!).
LoRA repository:
| #!/usr/bin/env python3 | |
| """ | |
| Very simple HTTP server in python for logging requests | |
| Usage:: | |
| ./server.py [<port>] | |
| """ | |
| import json | |
| from http.server import BaseHTTPRequestHandler, HTTPServer | |
| import requests |
First, here is my SimpleTuner LoRA config, so you can get started with it. I used an 80GB A100 (thanks @bghira!).
LoRA repository:
As quixel is being removed, all items are free to aquire. This script is to automate the process to add items to your account (As of writing, a total of 18874 items)
Note: This script only tested in the latest version of Chrome.
run.js)This is a little Gist I made to help others looking into buying a Framework mainboard for a homelab / NAS purpose. It's a mix of research I have undertaken to look for parts to work around the limited expansion capabilities of the Framework Desktop mainboard.
Bear in mind, this guide does not cheap out! It ensures that you get the best performance and reliability for your homelab or NAS setup for personal use only and maximises the potential of the Framework Desktop mainboard given its limitations. You may need to expand on my research to find the best components for your specific use case if your budget is more limiting.
While the Framework desktop mainboard is a beast in terms of its compute performance, and up to 128GB of unified memory, it only has two M.2 slots for storage expansion. This can be a limiting factor for users looking to build a high-capacity NAS or homelab environment.
| #!/usr/bin/env python3 | |
| """ | |
| Transplant extra tensors (e.g. MTP layers) from one GGUF file into another, | |
| producing a mixed-quantization GGUF. | |
| Note: Tested with ik_llama.cpp GGUF Python module. | |
| Usage: | |
| python convert.py <target.gguf> <source.gguf> <output.gguf> |