qlora-training
Complete guide to fine-tuning large language models using 4-bit quantization with QLoRA (Quantized Low-Rank Adaptation).
- Skills
- AI research
- 0
What it does and how to use it
A skill that teaches Claude Code to do ai research tasks well. It is packaged knowledge the model uses whenever it is relevant.
- Install the skill (command above).
- In Claude Code, describe what you need - the skill turns on by itself when the context fits.
- No commands to memorize: you talk in plain language and the skill does the rest.
Install (Claude Code)
mkdir -p .claude/skills/qlora-training && curl -fsSL https://doutorai.pt/data/lib/skills__qlora-training.md -o .claude/skills/qlora-training/SKILL.mdPaste this command into the terminal, in your project folder, and the component installs automatically.
Content
# QLoRA Training
Complete guide to fine-tuning large language models using 4-bit quantization with QLoRA (Quantized Low-Rank Adaptation).
## Overview
QLoRA enables fine-tuning 70B+ parameter models on consumer GPUs by:
- Loading base model in 4-bit (75% memory reduction)
- Training only small LoRA adapters (~20MB)
- Maintaining near-full-precision quality
**Memory savings**:
- Llama 2 70B: 140GB → 35GB (4-bit) + 20MB (LoRA) = **35GB total**
- Fits on single A100 80GB!
**Accuracy**: <1% degradation vs full fine-tuning
## Quick Start
### Basic QLoRA Fine-tuning
```python
from transformer
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