Lora (Low-Rank Adaptation)
Parameter-efficient fine-tuning technique that adapts large models with minimal trainable parameters.
About
LoRA, Low-Rank Adaptation by Microsoft Research, is a parameter-efficient fine-tuning method that injects trainable low-rank decomposition matrices into a frozen model's weights, cutting trainable parameters by orders of magnitude while keeping quality close to full fine-tuning. The loralib package integrates with PyTorch and Hugging Face models and is now part of the PEFT library. Released under the MIT license.
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Details
- Category
- Model Training & Fine-Tuning
- Price
- Free
- Platform
- Local/Desktop
- Difficulty
- Intermediate (3/5)
- License
- MIT
- Minimum VRAM
- 4 GB
- Added
- Apr 3, 2026
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