Remove the custom peft/ directory (LoRA/DoRA adapters, trainer, data
pipeline) in favor of a lightweight fine-tuning example that uses the
standard HuggingFace Transformers + PEFT ecosystem.
The new example at timesfm-forecasting/examples/finetuning/ demonstrates
LoRA fine-tuning via TimesFm2_5ModelForPrediction and the peft library,
based on the approach by @kashif at HuggingFace.
- Remove peft/ (8 files)
- Add timesfm-forecasting/examples/finetuning/finetune_lora.py
- Add timesfm-forecasting/examples/finetuning/README.md
- Update README.md to reference new example
- Clean up .gitignore (remove peft_checkpoints/)
- Add context limit rationale to system_requirements.md with memory formula
- Update SKILL.md to include XReg/covariates in description and usage sections
- Add dataset-aware memory estimation to check_system.py with new CLI args
- Document memory estimation in api_reference.md with Mermaid diagram
- Add dataset preflight section to SKILL.md with examples
Resolves review comments about:
- How context limits (512/1024) were determined
- Including XReg mode description in skill documentation
Bonus enhancement: Dataset preflight checking prevents OOM before loading data.
Rajat Sen