Month: 2026-04 — Focused delivery: Bias-Efficient Fine-Tuning (BEFT) feature in huggingface/peft with strong emphasis on parameter efficiency in low-data settings. Implemented BEFT by training only the bias terms of selected layers, enabling effective fine-tuning with reduced parameters. Commit 7d927c30f59d14e187170c556bf59804086d6767 documents the change (FEAT Add BEFT (#3195)) and references the BEFT paper (arXiv:2509.15974). BEFT is particularly effective when targeting the V projection under limited data; with abundant data or capacity, BEFT may be less optimal. Bug fixes: No major bug fixes reported for this month. Overall impact: Introduces a practical, parameter-efficient fine-tuning approach that lowers compute/memory costs and speeds up experimentation for low-resource scenarios, enabling broader adoption of PEFT techniques in production and research settings. Technologies/skills demonstrated: PyTorch/Transformers-based fine-tuning, parameter-efficient training methods, research-to-code translation, documentation and reproducibility (paper citation), attention to resource-constrained deployment.