Fine-tune Hugging Face LLMs with LoRA on Deadline Cloud

The hf_finetune_lora job bundle on the GitHub website fine-tunes a Hugging Face causal language model with Low-Rank Adaptation (LoRA) or Quantized Low-Rank Adaptation (QLoRA) on a custom instruction dataset, using the transformers, PEFT, and bitsandbytes libraries. The output is a small LoRA adapter (approximately 50 to 200 MB) that you load on top of the base model to teach it a writing style, a domain expertise, a specific output format, or some proprietary knowledge.

To run this bundle, you need a Deadline Cloud farm with a GPU-enabled queue (Linux fleet, NVIDIA GPU with 16 GB or more of video RAM (VRAM)) and a dataset in JSONL format. Submitting with all defaults trains on the bundle's included sample data. From the job_bundles directory of the samples repository, submit the job:

deadline bundle gui-submit hf_finetune_lora

For a complete walkthrough that covers dataset preparation, queue role permissions, fleet sizing, and testing the trained adapter, see Fine-tune Hugging Face LLMs with LoRA and QLoRA.