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unsloth

Fine-tune LLMs with unsloth for faster LoRA/QLoRA training and reduced VRAM usage.

Updated Oct 23, 2024
Search Tags:#fine-tuning#LLM#memory-efficient#unsloth#fast training
One-click install
npx skills add https://github.com/lenadlm/docker --skill unsloth-lenadlm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/lenadlm/docker/tree/main/hermes-skills/skills/mlops/training/unsloth
Command: npx skills add https://github.com/lenadlm/docker --skill unsloth-lenadlm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires unsloth, torch, transformers, trl, datasets, peft, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This skill provides comprehensive assistance with unsloth development, enabling faster and more memory-efficient fine-tuning of LLMs.

Core Features & Use Cases

  • Faster Fine-Tuning: Achieve 2-5x faster LoRA/QLoRA fine-tuning.
  • Reduced VRAM: Fine-tune LLMs with less VRAM usage.
  • Documentation Access: Access detailed documentation and code examples.
  • Use Case: For developers who want to fine-tune LLMs efficiently without compromising on performance.

Quick Start

Use the unsloth skill to fine-tune your LLM model with reduced VRAM requirements and increased speed.

Frequently Asked Questions about unsloth

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune an LLM with less VRAM using LoRA?▼

Fine-tuning an LLM with unsloth achieves 2-5x faster LoRA and QLoRA training speeds while significantly reducing VRAM usage. It streamlines the process for developers and researchers needing quick, resource-efficient model adaptation.

Can I use PyTorch and Hugging Face transformers for memory-efficient fine-tuning?▼

The unsloth skill integrates with PyTorch, transformers, and TRL dependencies to deliver memory-efficient fine-tuning. This allows developers to optimize LLM training speed and VRAM usage within existing Hugging Face workflows.

What is the best way to speed up QLoRA fine-tuning for large language models?▼

Using unsloth accelerates QLoRA fine-tuning by 2-5x compared to standard methods. It optimizes resource consumption, allowing developers to train large language models faster while maintaining performance standards.

Does unsloth support PEFT and datasets for efficient LLM training?▼

Unsloth includes PEFT and datasets as dependencies to enable efficient LLM training. This combination allows developers to achieve memory-efficient fine-tuning while preparing models for specific downstream tasks.

Why does standard LLM fine-tuning require so much VRAM and how can I reduce it?▼

Unsloth reduces VRAM usage during LLM fine-tuning by applying optimized LoRA and QLoRA mechanisms. This allows developers to train models locally or in the cloud without exceeding available GPU memory resources.

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