unsloth-qlora
CommunityExtreme VRAM efficiency for LLM fine-tuning.
Software Engineering#deep learning#qlora#unsloth#4-bit quantization#llm fine-tuning#vram optimization
Authorcuba6112
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill addresses the significant VRAM limitations encountered when fine-tuning large language models, enabling powerful training on consumer-grade hardware.
Core Features & Use Cases
- 4-bit Quantization: Utilizes Unsloth's dynamic 4-bit quantization to drastically reduce VRAM usage.
- High Accuracy Preservation: Maintains accuracy comparable to full fine-tuning by selectively preserving critical weights.
- Use Case: Fine-tune a 70B parameter model on a single 24GB GPU, achieving performance close to full fine-tuning without requiring enterprise-level hardware.
Quick Start
Load a 70B parameter model using the unsloth-qlora skill with 4-bit quantization enabled.
Dependency Matrix
Required Modules
unslothbitsandbytesaccelerate
Components
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: unsloth-qlora Download link: https://github.com/cuba6112/skillfactory/archive/main.zip#unsloth-qlora Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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