100% bootstrapped new startup with source available on GitHub. It lets you fine tune Mistral-7B and SDXL with a nice UI. In particular, for the LLM fine tuning we implemented a dataprep pipeline that turns websites/pdfs/doc files into question-answer pairs for training the small LLM using an big LLM.
It includes a GPU scheduler that can do finegrained GPU memory scheduling (Kubernetes can only do whole-GPU, we do it per-GB of GPU memory to pack both inference and fine tuning jobs into the same fleet) to fit model instances into GPU memory to optimally trade off user facing latency with vram memory utilization
It's a pretty simple stack of control plane and a fat container that runs anywhere you can get hold of a GPU (e.g. runpod).
It includes a GPU scheduler that can do finegrained GPU memory scheduling (Kubernetes can only do whole-GPU, we do it per-GB of GPU memory to pack both inference and fine tuning jobs into the same fleet) to fit model instances into GPU memory to optimally trade off user facing latency with vram memory utilization
It's a pretty simple stack of control plane and a fat container that runs anywhere you can get hold of a GPU (e.g. runpod).
Architecture: https://docs.helix.ml/docs/architecture
Demo walkthrough showing runner dashboard: https://docs.helix.ml/docs/overview
Run it yourself: https://docs.helix.ml/docs/controlplane
Roast me!