When I’m iterating on a small model or RAG setup, the hard part isn’t the training loop — it’s remembering why I chose a particular dataset split, which prompt variants I already tried, and what the eval notes meant two weeks later.
Right now I bounce between notebooks, a scratch doc, and chat logs, and the “why” of a run gets lost.
How do you keep experiment notes, data decisions, and eval commentary attached to the actual work so a future-you (or a teammate) can reopen it without archaeology?
Practical workflows welcome — folders, Spaces habits, local tools, whatever has stuck for you.
nootxlm 2
Make each run carry its own record instead of keeping notes beside it: when a run starts, save the dataset split, the prompt file, the model revision, and the eval command into that run’s folder automatically, then add only the part a machine cannot capture — three lines written at the moment of the choice saying what you picked, what you rejected, and what result would change your mind — with the scores appended to that same folder when the eval finishes. Test it by reopening one run two weeks later with the notebooks and chat logs closed: if you can say exactly what was tried, why, and what the numbers meant from that folder alone, the record is complete — and whichever question you cannot answer names the one field to add next time.