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Fine-tuningFreeOpen Source
QLORA
Fine-tune 65B LLMs on a single GPU with 4-bit quantization
MIT
ABOUT
Fine-tuning large language models requires prohibitive GPU memory — a 65B model needs multiple A100 80GB GPUs for full fine-tuning. QLoRA combines 4-bit NormalFloat quantization with Low-Rank Adapters (LoRA), cutting memory usage by 4x while preserving performance. A 65B model fits on a single 48GB GPU, making LLM fine-tuning accessible to anyone with consumer or mid-range hardware.
INSTALL
pip install bitsandbytes peft transformers accelerateINTEGRATION GUIDE
1. Fine-tune a 70B LLM on domain-specific data using a single consumer GPU with 4-bit quantization
2. Adapt an open-source model to a specialized task like legal document analysis or medical Q&A
3. Instruction-tune a base model on custom datasets while keeping memory usage under 24GB
4. Combine QLoRA with PEFT to train multiple lightweight adapters on top of one frozen model
5. Run comparative fine-tuning experiments across multiple model sizes on limited GPU hardware
TAGS
pythonfine-tuningquantizationlorallmtraininggputransformers