Memory-efficient fine-tuning with 4-bit quantization and LoRA adapters. Use when fine-tuning large models (7B+) on consumer GPUs, when VRAM is limited, or when standard LoRA still exceeds memory. Builds on the lora skill.
Installation
SKILL.md
QLoRA enables fine-tuning of large language models on consumer GPUs by combining 4-bit quantization with LoRA adapters. A 65B model can be fine-tuned on a single 48GB GPU while matching 16-bit fine-tuning performance.
Prerequisites: This skill assumes familiarity with LoRA. See the lora skill for LoRA fundamentals (LoraConfig, targetmodules, training patterns).
QLoRA introduces three techniques that reduce memory usage without sacrificing performance:
Memory-efficient fine-tuning with 4-bit quantization and LoRA adapters. Use when fine-tuning large models (7B+) on consumer GPUs, when VRAM is limited, or when standard LoRA still exceeds memory. Builds on the lora skill. Source: itsmostafa/llm-engineering-skills.
Facts (cite-ready)
Stable fields and commands for AI/search citations.
- Install command
npx skills add https://github.com/itsmostafa/llm-engineering-skills --skill qlora- Category
- </>Dev Tools
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- First Seen
- 2026-02-11
- Updated
- 2026-02-18
Quick answers
What is qlora?
Memory-efficient fine-tuning with 4-bit quantization and LoRA adapters. Use when fine-tuning large models (7B+) on consumer GPUs, when VRAM is limited, or when standard LoRA still exceeds memory. Builds on the lora skill. Source: itsmostafa/llm-engineering-skills.
How do I install qlora?
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/itsmostafa/llm-engineering-skills --skill qlora Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code or Cursor
Where is the source repository?
https://github.com/itsmostafa/llm-engineering-skills
Details
- Category
- </>Dev Tools
- Source
- user
- First Seen
- 2026-02-11