Resume LoRA training from a checkpoint
Continue a previous LoRA training run from its last saved checkpoint — for extending training after hitting a max-cost limit, or fine-tuning further after review.
Estimated $1–$6 per resumed run · 10–30 min
What inputs you need
- Checkpoint file from a prior run
- Same or updated dataset
- Training config (steps to add)
What Badgr returns
- Updated LoRA safetensors file
- Combined training loss curve
- Sample generations at new checkpoints
Recommended GPU routes
Estimated cost: $1–$6 per resumed run · Estimated runtime: 10–30 min
Example command
badgr run "python train_lora.py --resume checkpoint.safetensors --config lora.yaml" \
--gpu A100_40GB \
--max-cost 6 \
--max-runtime 40Common setup failures Badgr avoids
Optimizer state mismatch breaking a resumed run — Badgr's training image saves optimizer state alongside weights so resume behaves like an uninterrupted run.
Learning-rate schedule restarting from step zero on resume — config carries forward the original step count so the schedule continues correctly.
Ready to run this?
Launch from the dashboard, CLI, or Compute API. Max-cost protection included.