Back up AI prompts, LLM artifacts, and workflows
Preserve prompt versions, model references, retrieval inputs, and evaluation cases without confusing credentials with service credits.
Read the 6-minute guideAI workflows
LLM Insta Backups
Save the loading requirements too.
For a language model you are permitted to store locally, a useful archive includes the artifacts required to load and evaluate it. For a hosted model, focus on the identifiers, settings, inputs, and application context available to you. Access through an API does not imply access to export the model itself.
What to preserve
- Permitted weights, adapters, tokenizer files, and configuration.
- Dependency versions, loading instructions, and artifact identifiers.
- License context and a small, documented evaluation set.
Your starting workflow
Use the model framework’s supported save method and keep a manifest of the resulting files. Hugging Face documents saving model weights and configuration with save_pretrained. Add the rest of your project dependencies deliberately; a model save is not an application archive.
Test the recovered copy
Load from a working copy in an environment that does not rely on the original developer’s cache. Check which artifacts were actually used and run the documented evaluation. Record any missing file or runtime requirement in the recovery procedure.
Is a model checkpoint a complete workflow backup?
Not by itself. The workflow may also require prompts, source documents, application code, tool schemas, and evaluation cases. Keep these as a coherent release and test the task they are intended to perform.
From Insta Backups Lab
Put the plan into practice.