| title: "Efficient Multi-Model LoRA Training on Apple Silicon: Parallel Fine-Tuning of Seven 7B Specialists on a Single Workstation — Hayula Research" | |
| authors: | |
| - Hayula AI Lab | |
| papers: | |
| - paper.md | |
| tags: | |
| - lora | |
| - training | |
| - commodity-hardware | |
| - multi-model | |
| license: mit | |
| # Efficient Multi-Model LoRA Training on Apple Silicon: Parallel Fine-Tuning of Seven 7B Specialists on a Single Workstation — Hayula Research | |
| **Hayula AI Lab** | |
| ## Abstract | |
| We present a methodology for parallel LoRA fine-tuning of multiple large language models on a single Apple M2 Ultra workstation with 192GB unified memory. Using the Saif cybersecurity suite—eight 7-billion parameter specialist models (Router, Reverse Engineering, Vulnerability Discovery, Exploit Chain Development, Kernel Security, Browser Security, Web Application Security, and Network Security)—we demonstrate that simultaneous training of up to six LoRA adapters is not only feasible but signifi | |
| ## Files | |
| | File | Description | | |
| |------|-------------| | |
| | `paper.md` | Full paper (Markdown) | | |
| | `README.md` | This model card | | |
| ## Citation | |
| ```bibtex | |
| @techreport{hayulalab2026efficientmultimodeltraining, | |
| title={Efficient Multi-Model LoRA Training on Apple Silicon: Parallel Fine-Tuning of Seven 7B Specialists on a Single Workstation — Hayula Research}, | |
| author={Hayula AI Lab}, | |
| year={2026}, | |
| url={https://huggingface.co/hayulalab/efficient-multi-model-training-paper} | |
| } | |
| ``` | |
| --- | |
| *hayulalab — Open Source AI Research* | |