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We’ve trained models and pushed them to registries. But before putting them into production, there’s one critical step: fine-tuning the model on your own data.

There are several methods out there, but IdeaWeaver simplifies the process to a single CLI command.

It supports multiple fine-tuning strategies:

• full: Full parameter fine-tuning
• lora: LoRA-based fine-tuning (lightweight and efficient)
• qlora: QLoRA-based fine-tuning (memory-efficient for larger models)

Here’s an example command using full fine-tuning:

ideaweaver finetune full \
  --model microsoft/DialoGPT-small \
  --dataset datasets/instruction_following_sample.json \
  --output-dir ./test_full_basic \
  --epochs 5 \
  --batch-size 2 \
  --gradient-accumulation-steps 2 \
  --learning-rate 5e-5 \
  --max-seq-length 256 \
  --gradient-checkpointing \
  --verbose

No need for extra setup, config files, or custom logging code. IdeaWeaver handles dataset preparation, experiment tracking, and model registry uploads out of the box.

Docs: https://ideaweaver-ai-code.github.io/ideaweaver-docs/fine-tuning/commands/
GitHub: https://github.com/ideaweaver-ai-code/ideaweaver

If you're building LLM apps and want a fast, clean way to fine-tune on your own data, it's worth checking out.