TRLoom turns Hugging Face TRL fine-tuning into a single YAML config. Describe your model, dataset, trainer, W&B, and optional Modal GPUs — then run one command. `pip install trloom` `trloom run config.yaml` `trloom run config.yaml --modal` Built for ML engineers and researchers who want SFT, DPO, GRPO, and more without rewriting boilerplate every experiment. Open source (Apache 2.0).
Hey Product Hunt 👋
I’m Saqlain - I kept rewriting the same Hugging Face TRL setup for every SFT / DPO / GRPO run: model loading, datasets, trainers, W&B, then Modal when I needed GPUs.
So I built TRLoom: one YAML file → one command.
• TRL-native trainers (follows whatever your installed TRL exposes)
• Hub or local datasets
• W&B from YAML
• Same config on Modal with --modal
Try it:
pip install trloom
trloom run config.yaml
Docs: https://saqlain2204.github.io/tr...
GitHub: https://github.com/saqlain2204/t...
Curious what you’d run first — local SFT, DPO, or a Modal GPU job? Feedback and issues welcome.