llm-training-workflows

Fine-tune large language models with LoRA or QLoRA by training lightweight parameter-efficient adapters instead of the entire model. This PEFT approach drastically reduces compute and memory requirements while maintaining performance for downstream tasks.

Distributed training with PyTorch FSDP shards model parameters, gradients, and optimizer states across multiple GPUs and nodes. This enables efficient scaling for large language models that exceed single-GPU memory limits during training.

Yes, you can use GRPO with TRL for reinforcement learning post-training. This combination enables structured output generation and reward-based alignment to refine large language model behavior for production inference workflows.

The best way to serve multiple LoRA adapters in production is through multi-adapter serving patterns. This approach allows scalable deployment of multiple fine-tuned PEFT adapters within a single inference workflow, reducing infrastructure overhead.

A multi-GPU setup is required for PyTorch FSDP distributed training but not strictly for QLoRA fine-tuning. QLoRA is designed to run on single GPUs, while FSDP specifically targets multi-GPU and multi-node clusters for larger models.

Use parameter-efficient fine-tuning instead of full model training to reduce compute, memory, and development time. PEFT trains small adapter modules rather than whole models, achieving comparable results with a fraction of trainable parameters.